# Can Machine Learning Uncover Insights into Vehicle Travel Demand from Our
Built Environment?

Zixun Huang<EMAIL_ADDRESS>Hao Zheng  University of California at
Berkeley, CA, USA  City University of Hong Kong, Hong Kong, China

###### Abstract

In this paper, we propose a machine learning-based approach to address the
lack of ability for designers to optimize urban land use planning from the
perspective of vehicle travel demand. Research shows that our computational
model can help designers quickly obtain feedback on the vehicle travel demand,
which includes its total amount and temporal distribution based on the urban
function distribution designed by the designers. It also assists in design
optimization and evaluation of the urban function distribution from the
perspective of vehicle travel. We obtain the city function distribution
information and vehicle hours traveled (VHT) information by collecting the
city point-of-interest (POI) data and online vehicle data. The artificial
neural networks (ANNs) with the best performance in prediction are selected.
By using data sets collected in different regions for mutual prediction and
remapping the predictions onto a map for visualization, we evaluate the extent
to which the computational model sees use across regions in an attempt to
reduce the workload of future urban researchers. Finally, we demonstrate the
application of the computational model to help designers obtain feedback on
vehicle travel demand in the built environment and combine it with genetic
algorithms to optimize the current state of the urban environment to provide
recommendations to designers.

###### keywords:

Vehicle Travel Demand; Urban Data Mining; Land Use Optimization; Neural
Networks

††journal: Cities

##  1 Introduction

###  1.1 Background

A significant number of empirical studies demonstrate the interdependence
between land use and local vehicle travel demand through household surveys
(Cervero,, 1991, 1996; Cervero and Duncan,, 2003). For example, researchers
have shown that mixed-use development (MXD) promote the alternative role of
non-motorized travel choices such as walking and cycling (Cervero and
Kockelman,, 1997; Maria Kockelman,, 1997; Park et al.,, 2018), and high
density facilitates the use and development of public transit mode (Cervero,,
1994; Dunphy and Fisher,, 1996). And recent researchers have revealed that
both the abundance and spatial configuration of urban land use are correlated
with traffic congestion(Wang and Debbage,, 2021).

Management of traffic resources using an urban design perspective proves to be
feasible and effective (Cervero and Kockelman,, 1997; McNally and Kulkarni,,
1997). These urban studies receive significant attention to quantify and
measure travel behavior, built environment, and the correlation between the
two (Song et al.,, 2013; Ewing and Cervero,, 2001; Clifton et al.,, 2008;
Ewing and Cervero,, 2010). To encourage efficient utilization of traffic
resources and improve the relation between traffic supply and demand, there
exists tools for policymakers and urban designers to consider involving
traffic analysis and prediction from the perspective of land use (Wachs,,
1989).

###  1.2 Problem Statement

However, deficiencies and omissions in past studies hamper more detailed
conclusions that help urban designers determine the relationship between
vehicle travel demand and urban functions and then to carry out a better urban
design.

The prevailing measurement methods of urban environment used in these studies
omit specific information regarding urban function ratio, thus failing to
provide urban designers with more detailed references. The entropy index, for
example, illustrates this shortcoming. Different proportions of urban
functions may produce the same entropy value, and the highest one hinders the
designers in crafting the most appropriate LMU strategy (Song et al.,, 2013).
Occasionally, the variances in research subjects, such as neighborhoods, are
consolidated into a single categorical variable with a concomitant loss of
information (Ewing and Cervero,, 2001), including but not limited to function
distribution information. However, some researchers have studied the influence
of specific urban functions such as offices, residences, retail and transit on
vehicle travel behaviors and demonstrated the impact of specific urban
functions on transportation travel (Cervero and Duncan,, 2006; Systematics et
al.,, 1994; Choi et al.,, 2021).

Limited information and dimensionality of survey data left a dearth of detail
on the impact of urban function distribution on local vehicle travel demand
temporal distribution. Peak congestion serves as a major challenge for urban
traffic networks with limited bearing capacity. Average distribution of the
traffic in time can help share the traffic pressure (Loudon et al.,, 1988;
Downs,, 2005; Shallal and Khan,, 1980). Cervero suggested in his early
research that trips tend to be more evenly distributed throughout day and week
in a more mixed-function community (Cervero,, 1996, 1989). While most
literature in this field mainly focus on the impact of land use on the total
amount of traffic, such as vehicle miles traveled (VMT) and vehicle hours
traveled (VHT), few studies focused on and made conclusions the relationship
between urban land use and temporal distribution of vehicle travel demand.

Empirical evidence struggles to be repeated in new study areas, cultures, and
eras. Traditional travel behavior modeling relies on labor-intensive, high-
cost, and time-consuming data collection methods such as American Household
Surveys and local travel surveys that make large scale, in-time analysis
extremely difficult to achieve (Li et al.,, 2020). Past empirical evidence on
the transportation impact description of built environments rendered itself
inconsistent (Cervero,, 1996; Cervero and Duncan,, 2006) and unsuitable for
comparison due to lacking reliable standard error estimates from individual
studies (Ewing and Cervero,, 2010). For instance, Cervero utilized building
height as a proxy for employment density (Cervero,, 1991) while others use
gross population density (Dunphy and Fisher,, 1996) to measure a similar
problem. Thus, designers often spend high labor costs to verify the practical
effects of the empirical conclusions in a different region.

In summary, researches based on the traditional survey method have provided
abundant conclusions of built environment factor, including but not limited to
local density, diversity, and single-function distribution, and its impact on
travel behavior. Designers find inspiration based on these findings to
construct a better planning decision. However, these research methods lack
further detail regarding the spatial patterns of urban functions as well as
the temporal patterns of vehicle travel demand. They also fail to illustrate
the relationship between them and produce viable conclusions in other
different areas.

###  1.3 Data-driven and Machine Learning Based Urban Study

With the development of artificial intelligence (AI) and urban information
management, urban designers acquire opportunities to enhance traditional
workflow and analyze in detail the traditional urban problem (i.e., how urban
function distribution affects vehicle travel demand) from integrated
quantitative and qualitative perspectives. Rapid advancement in information
and communications technology has facilitated a generation of data capturing
human movements and daily activities (Li et al.,, 2020; Chauhan et al.,,
2016). An increasing number of urban researchers have adopted ubiquitous urban
data collected in real-time (Kang et al.,, 2020; Shen and Karimi,, 2016;
Gervasoni et al.,, 2016; Yuan et al.,, 2012) to quantify and describe urban
patterns and processes. Additionally, machine learning (ML) related algorithms
have been used to create models that perform predictive functions in data-
driven urban study (Nosratabadi et al.,, 2019; Souza et al.,, 2019). The
consensus states that ubiquitous urban data combined with ML technology bears
tremendous potential in the field of urban studies and smart city solutions
(Nosratabadi et al.,, 2019; Hancke et al.,, 2013; Ibrahim et al.,, 2020;
Hadjimichael et al.,, 2016; Youssef et al.,, 2020).

When applied, ML algorithms are capable of automating urban tasks such as land
use and land cover (LULC) mapping and zoning (Demir et al.,, 2018; Kandrika
and Roy,, 2008); predicting future urban evolution (Grekousis et al.,, 2013);
mapping residential density patterns jointly with multi-temporal Landsat data
(McCauley and Goetz*,, 2004); discovering function regions with human mobility
and points of interest (POI) data (Yuan et al.,, 2012); identifying land-use
type based on temporal traffic patterns with taxi data (Liu et al.,, 2012);
automatically evaluating the urban visual environment in a large scale (Liu et
al.,, 2017); estimating health outcomes at a neighborhood scale (Feng and
Jiao,, 2021).

Related research on the application of ML in travel behavior includes applying
decision tree (DT) induction (Loh,, 2011) to predict transport mode (Wets et
al.,, 2000) and route choice (Yamamoto et al.,, 2002; Arentze et al.,, 2000).
ML can also be used for exploring the relationship between ride-sourcing
services and vehicle ownership (Sabouri et al.,, 2020). Deep learning
approaches to predict the short-term demand of bike-sharing FFBS with weather
and air quality data (Bao et al.,, 2019) and to develop a prediction system
for real-time parking in smart cities (Vlahogianni et al.,, 2016).

###  1.4 Objective

This research aims to develop a method capable of creating a computational
model that maps detailed built environment information into vehicle travel
demand information including temporal distribution and total amount. Such a
model, therefore, aids urban designers in receiving feedback quickly and
decision-makers in optimizing the allocation of resources to achieve a balance
between traffic and other urban purposes like residence, commerce, industry,
and infrastructure.

This research combines travel behavior data and urban function distribution to
generate a machine learning model that offers vehicle travel demand
prediction. We developed an ML model that learns experiences from urban data
to help designers automate detailed traffic demand judgments in a certain area
of a given city. Regarding the problem statement, the prediction model of this
study is based on three setups.

  1. 1.

An input feature about local built environment defined as a ratio between
urban functions in the region of study instead of a single categorical
variable or a measure of land use mix;

  2. 2.

An output feature about travel behavior defined as the average daily quantity
and temporal distribution of vehicle travel demand in study region instead of
VMT or VHT;

  3. 3.

Commonly used open-source urban data utilized as training data sets for the
prediction model to examine the cost efficiency of applying this research
method to different cultural areas.

This research aspires to use a computer, which inputs detailed urban function
distributional information to the program, to predict the average daily
quantity and temporal distribution of vehicle travel demand of a given local
area. The temporal distribution image of vehicle travel demand becomes an
output regarded as a reference to adjusting urban function distribution within
the area. Then, the ML model is reused to verify the adjusted urban function
distribution and determine whether the revision achieves a purpose that makes
more rational use of traffic resources. Furthermore, urban function
composition information can be iteratively optimized by combining the
prediction model with a genetic algorithm.

##  2 Methodology

This section explains the method, primarily regarding data preparing,
sampling, and model training. Figure 1 shows a general introduction to the
production of a more detailed forecast of vehicle travel demand. First, data
about the built environment and the travel demand should be collected and
quantified through a web crawling and data cleaning process. After that, the
data-set is redivided into a series of data samples based on geographic
location. Subsequently, forecasting models such as Artificial Neural Networks
map the relationship between the urban environment and vehicle travel demand
based on the processed samples. The prediction models should serve as a guide
for designers and decision-makers to interpret environmental information and
meet the crowd travel demands.

###  2.1 Data Preparing and Feature Sample Rule

To describe the urban local function environment in detail, points of interest
(POI) are selected as elements that form our living environment. Urban
function density and mix in local areas of different scales, such as in
function zones (Yao et al.,, 2017); in neighborhoods (Yue et al.,, 2017); in
living areas (Liu et al.,, 2020); in 1km buffers (Zhao et al.,, 2018); and at
the census tract level (Maharana and Nsoesie,, 2018), are measured as the
quantitative relationship between POI. In this study, we describe our urban
function environment in the following way: the total number of POI types
defined as $k$; the amount of POI type $j$ in a local area $Z$ denoted as
$x_{Z}^{j}$; the density of all urban function types within the district $Z$
measured by proxy as $X_{Z}=\sum_{j=1}^{k}x_{Z}^{j}$; and the percentage of
the urban function $j$ measured as $p_{Z}^{j}=\frac{x_{Z}^{j}}{X_{Z}}$. It
follows that $\sum_{j=1}^{k}p_{Z}^{j}=1$.

To obtain an urban function spatial distribution containing latitudinal and
longitudinal information, we choose AutoNavi as the data source, which is a
Chinese web mapping, navigation and location-based services provider. Based on
POI code table provided by AutoNavi, urban functions are divided into 16
categories (Table 8).

Figure 1: Data pre-processing and neural network training method.

According to the API user guide, city code and POI code are initially selected
as main crawling input parameters. However, we discovered that POI in each
category are limited to 900 points with city code as a parameter input, which
causes a sparse urban function distribution and leads to unreasonable equaling
of different POI types. The urban function environment cannot be described
accurately in this situation. To solve this problem, the entire target area is
divided into smaller equal parts for finer crawling to acquire information on
the built environment that more accurately reflects reality. The POI
information in each part is then captured and categorized by POI types (Figure
2).

Figure 2: The process of built environment information crawling

After obtaining the detailed urban function distribution data, we sample the
whole urban area uniformly for input features for machine learning later. The
feature sampling rule follow as such (Figure 3):

Figure 3: The flow chart of the feature sampling rule

  1. 1.

A buffer area with a radius of 1 km, a standard unit of measurement in urban
researches (Zhao et al.,, 2018; Frank et al.,, 2004, 2005; Sallis et al.,,
2016) regarding the relationship between land use and travel behavior, defined
as a basic sampling unit;

  2. 2.

The whole area of interest sampled every 200 m (i.e., general land block
scale);

  3. 3.

A sampling unit defined as region $Z$, the proportion of urban functions
$p_{Z}^{j}$ and density $X_{Z}$, and measured as urban environment features of
this unit. In addition, we normalize the density feature of each sample. The
maximum density proxy value of all cells is denoted as $D$, and the normalized
density value of each sample is redefined as $X_{Z}^{*}=\frac{X_{Z}}{D}$. The
total number of samples is defined as $n$. For sample $Z$ ($Z$=1-$n$), the
final 17 built environment features obtained by this sampling method are
$X_{Z}^{*}$ and $p_{Z}^{j}$ ($j$=1-16), which describe the whole density of
urban functions and detailed proportion of 16 urban functions in the study
sample $Z$.

Pertaining to vehicle travel demand data, this research adopts open-source
data. Taxi as a door-to-door, all-weather way of travel becomes the subject of
study. Vehicle travel demand in a certain place can be represented by taxi
orders or vehicle hours traveled (VHT) generated by those orders. The
regression model developed by Liu (Liu et al.,, 2020) shows strong links
between demand for taxis and urban form characteristics. For a large body of
emergent data related to travel behavior (Liu et al.,, 2012; Davis et al.,,
2016; Tong et al.,, 2017; Zhang et al.,, 2017), taxi data proves to be an
excellent option because of its ease of acquisition and large data size.
Valuable actual and general data are generated along due to a mass of real
behaviors. Moreover, vehicle hours traveled (VHT), which has been proven
significant in environment research, is ultimately selected as the fundamental
travel variable as a result of convincing, prior empirical studies by Ewing
and Cervero about processing travel behavior data. (Ewing and Cervero,, 2001).

Traffic order data are distributed evenly to each day and then divided into 24
intervals based on the time of order. Subsequently, these data are sampled in
the same way mentioned above. In addition, information on hours traveled is
extracted from the orders to describe exact travel demand based on Cervero’s
previous research. Therefore, each piece of order is bound with an additional
weight coefficient representing VHT generated by order data. For a geographic
unit $Z$ sampled, the average daily order is counted as $c_{Z}=\sum VHT$. The
orders between $i$ o’clock and $i+1$ o’clock are formulated as $c_{Z}^{i}$
($i$=0-23). The order distribution ratio in each interval is marked as
$q_{Z}^{i}=\frac{c_{Z}^{i}}{c_{Z}}$. Therefore, it follows that
$\sum_{i=0}^{23}q_{Z}^{i}=1$; For sample $Z$, the final 25 travel demand
features obtained by this sampling method are normalized $c_{Z}^{*}$ and
$q_{Z}^{i}$ ($i$=0-23), which describe the whole density of travel demands and
detailed VHT distribution throughout a day in study sample $Z$.

Figure 4: Heat distribution maps of 16 kinds of urban functions (sorted by
Table 8). For each map, the brighter grid region contains the more function
points. Figure 5: Heat distribution maps of 24 segments of VHT (from 0 o’clock
to 23 o’clock). For each map, the brighter grid area represents the higher
total VHT value compared with other grids.

###  2.2 Study Area

A port city in China named Haikou recently approved international tourism
island construction. Consequently, a rapid increase of vehicle ownership and
urban road traffic gradually became obstacles restricting the development of
its social economy. The high sand content due to Haikou’s special geology
poses the risks of surface subsidence and seawater back-up. Thus, it is
difficult to establish a subway system as public transportation to relieve the
pressure of traffic.

A total of 65680 POI data of Haikou is obtained through a Python program
called API provided by AutoNavi. A month’s amount of taxi order data in Haikou
totaling 1,809,517 is provided by Didi Chuxing GAIA Initiative, an open-source
data set. According to feature sampling rules, 15664 sample units were
collected from the location of $(110.138,19.909)$ to $(110.494,20.104)$ in the
GCJ02 coordinate system. Python is used to grid the geographical scope of
Haikou and to calculate 17 built environment features and 25 travel demand
features for each grid in this process.

Only 5991 valid units are reserved for neural network training after data
cleaning operations to ensure that all samples contain valid built environment
information and effective travel requirements. Samples that fail to contain
any POI information or exceed one taxi order per hour on average in its
radiation buffer are excluded.

In addition, (Figure 4) and (Figure 5) illustrate the urban function and
travel data demand respectively collected in Haikou. Although not the focus of
this research, they help us better understand the characteristics of the site
in order to evaluate the prediction results of the computational model.
Through these two sets of maps, we macroscopically see the distribution of
regional centers of different functions and how active vehicle travel demand
flows over time.

###  2.3 Neural Network Training and Accuracy Definition

This research endeavors to formulate computational models that predict vehicle
travel demand that also include information about traffic temporal
distribution with respect to the density and composition of urban functions.
The prediction models should therefore help designers adjust and optimize
their urban planning.

ANNs, Random Forest (RF), and Support Vector Machine (SVM) are chosen as the
algorithms to obtain a computational model applicable to this study. ANN-based
models, in particular, utilize Back-Propagation (BP), which fine-tune the
weights of neurons to find practically accurate solutions for formulated
problems and phenomena only understandable through experimental data and field
observations (Basheer and Hajmeer,, 2000). They fulfill the problem of
connections between our built environment and vehicle travel demand.

Understanding the complexity in both forecasting total demand and acquiring
detailed information, we constructed different structures of neural networks
referred to as network T and D. The built environment information processed by
feature sampling rules can be initially set as 17 input neurons with values
ranging from 0 to 1, indicating a proportion of 16 urban functions and a
normalized density compared with the densest area. As for the output
structures, the number of neurons is determined by the number of parameters
needed to predict different network structures. For network T, total vehicle
travel demand is set as one output neuron with a value from 0 to 1, indicating
the normalized total demand compared with the most active area. For network D,
detailed temporal distribution is set as 24 output neurons with a value from 0
to 1, indicating a 24-hr VHT distribution ratio. In addition, sigmoid function
and mean squared error (MSE) are selected as activation and loss functions
when training our neural networks.

The number of hidden layers and neurons therein should also be defined. Based
on the literature (Karsoliya,, 2012) and actual tests, suitable results are
obtained when the number of neurons of each hidden layer is 36 for network T
and 82 for D. Five-fold cross-validation tests are employed to find neural
network structures catering to the complexity of our study problem. Accuracy
functions are customized to ascertain the loss function and help us better
evaluate the accuracy of the outputs.

Equation 1 is used to evaluate the prediction accuracy of the vehicle travel
demand density (the total VHT amount all over the day). In this formula, we
take the absolute value of the difference between the predicted value of the
normalized local total amount of VHT and its ground-truth value as the error
value.

$Accuracy=1-\frac{\lvert
groundtruth_{allday}-prediction_{allday}\rvert}{groundtruth_{allday}}$ (1)

Equation 2 evaluates the prediction accuracy of detailed VHT temporal
distribution. With it, we take the sum of the absolute value of the difference
between the predicted values of VHT per hour and their ground truth value as
error.

$Accuracy=1-\sum_{clock=0}^{23}\lvert
groundtruth_{clock}-prediction_{clock}\rvert$ (2)

ANN Structure | Median accuracy for detailed temporal distribution (%) | Median accuracy for local demand density (%)
---|---|---
2- Layer ANN | 95.86 | 91.6
3- Layer ANN | 96.52 | 93.86
4- Layer ANN | 97.07 | 94.1
5- Layer ANN | 97.57 | 94.81
6- Layer ANN | 97.48 | 95.52
7- Layer ANN | 97.78 | 95.68
8- Layer ANN | 97.84 | 95.22
9- Layer ANN | 97.57 | 95.1
Others | - | -
Random Forest | 91.78 | 91.88
Linear SVR | 92.96 | 91.61
Table 1: Five-fold cross-validation of neural networks with different numbers
of layers.

We tested our neural networks on parts of the Haikou region using the
processed environment information as the inputs and the processed demand data
as the outputs. A total of 898 feature samples are divided into 5 parts, four
for training and one for validation. Table 1 presents the median accuracy of
neural networks with different layer sizes in the fivefold cross-validation
test. We also compared our artificial neural networks with other types of
machine learning models such as the RF and Linear SVM. ANN noticeably achieves
higher accuracy at least for predictions in the local area with randomly
distributed samples. Meanwhile, the artificial neural network T with 7 layer-
size sports the highest accuracy in the vehicle travel demand prediction of
local total demand while the neural network D with 8 layers is most accurate
in the demand prediction of detailed temporal distribution. As a result, the
neural networks with 7 layer-size and 8 layer-size are chosen as the
preliminary settings. In addition, we used a batch size of 100 in the
preliminary settings and the customized error rate (1- accuracy) with
increasing epochs recorded during the experiments to help us find an
appropriate number of iterations in prediction model training (Figure 6).

###  2.4 Post Process and Visualization

Using the trained model, the predictions of vehicle travel demand information
are remapped into values before normalization. They respectively represent the
total VHT values throughout one day and the proportions of VHT produced in 24
one-hour periods on geographic units. In addition, the prediction values are
then visualized and compared with the ground truth values.

##  3 Result and Application

###  3.1 Accuracy of Training Set and Test Set

Based on feature sampling rules and the training method settings mentioned
above, two forecast models at various regional scales are built and trained
with different data sets. Up to 5991 valid samples, inclusive of the built
environment and vehicle travel demand information, represent the whole area of
Haikou. 898 samples signifying the Haikou urban area (Figure11) are used as
data sets to test the accuracy of the two neural networks. In the case of
random geographic distribution, 80 percent of samples are used in the training
process, and the remaining samples are used in accuracy tests.

Figure 6: This figure presents the error rate variation of the prediction
model in the urban area of Haikou with the number of iterations increasing.
The red lines represent total VHT amount prediction error rates and the blue
lines represent VHT temporal distribution prediction error rates; the bright
lines represent the error rates variation in the test set, and the faded lines
represent the error rates variation in the training set. Figure 7: The same as
Figure 6. This figure presents the error rate variation of the prediction
model in the whole area of Haikou with the number of iterations increasing.
Figure 8: The prediction error rates of daily total VHT amount, Right: trained
with samples having accumulated VHT of less than 2000 hours within a month,
Left: trained with samples having accumulated VHT of more than 2000 hours
within a month.

Figure 6 and 7 present the error rates of the demand forecast models both in
the urban and whole area of Haikou, showing they fall to a convergent state as
the number of training iterations increases. The results plotted in the
figures fulfill expectations regarding the prediction accuracy of the Haikou
urban model test: 95.68% for the average total VHT throughout one day, 97.84%
for the VHT temporal distribution prediction. The prediction accuracy of the
Haikou all-region model test boasts an accuracy of 85.77% for the average
total VHT throughout one day, 95.17% for the VHT temporal distribution
prediction.

Nine samples from the all-region model test were randomly calculated, had
their prediction outputs visualized, and then compared to the ground truths.
The built environment inputs and the detailed demand predictions are displayed
in the table 2 and 3. The bright lines of Figure 9 present the prediction
results of these instances calculated by our neural network, and the
corresponding ground truths represented by the faded lines are also plotted in
these figures to make a comparison (i.e., ST82, ST271, ST279, ST490, ST673,
ST912, ST932, ST990, and ST1077 represent different samples from the test set,
which annotate their numerical positions). Figure 10 depicts the predictions
and the ground truths of total VHT throughout one day.

An accuracy of 85.77% in the average total VHT prediction of Haikou is a
relatively low value when compared with the results draw from exclusively its
urban area, but the prediction results seem acceptable when visualized into
circle areas. Most cases with low accuracy occur in remote places that have
few and random travel orders compared with those located in urban areas. An
understandable reason for this discrepancy is the lack of travel orders to
form a consistent and regular pattern in these samples; therefore, slight
differences in absolute value lead to notable error rates as ST271 shows in
figure 10.

To determine if samples with sparse travel activity cause the problem of
accuracy, we roughly divided the all-region data set into two parts. One
contains 3065 samples, each of which accumulated VHT of no more than 2000
hours within a month (Set-A), and the other part contains 2935 samples that
each accrued VHT above 2000 hours (Set-B). Two computational models are
therefore trained with these two data sets and preliminary network structure.
Figure 8 plots the median error rates of both models’ predictions in daily
average total VHT amount. They show that convergence and overfitting of the
model trained by Set-A occurred at approximately 1000 epochs, and that
accuracy sits at a relatively low at 83.27%. After removing the data samples
with low travel activity, the model trained with the retained data shows a
higher accuracy rate of 92.59% and a stabler error fitting curve.

The results are visualized and analyzed for VHT temporal distribution
predictions as well. Our calculation model sports a significant fitting effect
on the detailed temporal proportion distribution of travel demand. According
to the same problem above, some samples in remote places without abundant
vehicle orders to produce VHT incompletely fit the truths as ST990 shows,
which has an accuracy of 64.17% below the median accuracy.

In conclusion, we received satisfying median accuracy results about the neural
network used to map a link between a given built environment and vehicle
travel demand prediction. The detailed temporal distribution of the demand
offers acceptable median accuracy as well. For total demand, despite the
presence of samples with insufficient travel activities muddling their demand
patterns, the magnitude of the total remains relatively correct and
distinguishable from samples with significant travel activities through the
visualization process. These verify the feasibility of our computational
models to forecast vehicle travel demand in different periods, and these
models perform better in areas with frequent human activities compared to
remote ones.

Inputs | ST82 | ST271 | ST279 | ST490 | ST673 | ST912 | ST932 | ST990 | ST1077
---|---|---|---|---|---|---|---|---|---
poi 00 | 6.94% | 8.62% | 1.38% | 4.32% | 3.57% | 7.82% | 10.22% | 2.38% | 3.54%
poi 01 | 14.30% | 10.92% | 17.59% | 9.31% | 5.95% | 8.64% | 14.06% | 7.14% | 9.60%
poi 02 | 10.93% | 7.47% | 19.66% | 6.06% | 16.67% | 31.48% | 9.74% | 26.19% | 11.11%
poi 03 | 17.87% | 8.62% | 15.17% | 9.31% | 3.57% | 4.73% | 17.57% | 11.90% | 13.13%
poi 04 | 2.94% | 3.45% | 6.55% | 8.59% | 0.00% | 1.03% | 1.60% | 0.00% | 2.02%
poi 05 | 6.87% | 5.17% | 1.03% | 5.31% | 2.38% | 1.03% | 4.47% | 0.00% | 2.53%
poi 06 | 6.24% | 0.57% | 1.38% | 8.63% | 1.19% | 1.65% | 3.51% | 7.14% | 5.56%
poi 07 | 0.56% | 1.15% | 1.72% | 0.44% | 0.00% | 0.82% | 0.32% | 0.00% | 0.51%
poi 08 | 6.24% | 1.72% | 1.38% | 6.26% | 0.00% | 2.47% | 5.91% | 0.00% | 13.13%
poi 09 | 8.69% | 32.76% | 14.48% | 13.66% | 33.33% | 20.58% | 11.50% | 11.90% | 7.58%
poi 10 | 7.85% | 5.17% | 7.93% | 5.23% | 10.71% | 6.58% | 7.03% | 2.38% | 5.05%
poi 11 | 4.48% | 9.77% | 4.48% | 12.71% | 20.24% | 9.05% | 8.31% | 23.81% | 17.68%
poi 12 | 0.84% | 0.00% | 0.34% | 0.04% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00%
poi 13 | 1.33% | 1.15% | 0.69% | 0.83% | 2.38% | 2.67% | 2.40% | 2.38% | 4.04%
poi 14 | 2.80% | 2.30% | 1.03% | 7.80% | 0.00% | 0.00% | 1.60% | 0.00% | 1.52%
poi 15 | 1.12% | 1.15% | 5.17% | 1.50% | 0.00% | 1.44% | 1.76% | 4.76% | 3.03%
Overall | 1427 | 174 | 290 | 2525 | 84 | 486 | 626 | 42 | 198
Table 2: The input table for Figure 9. Outputs | ST82 | ST271 | ST279 | ST490 | ST673 | ST912 | ST932 | ST990 | ST1077
---|---|---|---|---|---|---|---|---|---
vht 00 | 1.09% | 1.19% | 1.93% | 1.92% | 1.19% | 0.77% | 1.14% | 0.73% | 0.81%
vht 01 | 0.66% | 0.54% | 1.27% | 1.45% | 0.54% | 0.44% | 0.48% | 0.52% | 0.44%
vht 02 | 0.38% | 0.30% | 1.03% | 0.95% | 0.34% | 0.16% | 0.34% | 0.40% | 0.23%
vht 03 | 0.25% | 0.21% | 0.47% | 0.61% | 0.21% | 0.10% | 0.15% | 0.09% | 0.16%
vht 04 | 0.33% | 0.32% | 0.46% | 0.51% | 0.33% | 0.19% | 0.37% | 0.16% | 0.28%
vht 05 | 0.50% | 0.49% | 0.50% | 0.48% | 0.33% | 0.53% | 1.17% | 0.21% | 0.84%
vht 06 | 1.16% | 1.11% | 0.79% | 0.92% | 0.94% | 1.15% | 2.16% | 0.63% | 1.63%
vht 07 | 3.44% | 2.50% | 2.17% | 2.93% | 1.21% | 2.48% | 6.42% | 1.18% | 5.86%
vht 08 | 4.87% | 3.41% | 3.20% | 4.11% | 4.64% | 3.28% | 6.59% | 2.04% | 6.80%
vht 09 | 5.56% | 4.40% | 3.91% | 4.42% | 5.46% | 4.80% | 6.08% | 3.84% | 6.23%
vht 10 | 6.02% | 4.60% | 4.56% | 4.47% | 9.11% | 5.00% | 5.59% | 5.15% | 5.48%
vht 11 | 6.90% | 3.95% | 5.33% | 4.84% | 9.11% | 6.38% | 5.31% | 7.53% | 6.41%
vht 12 | 5.57% | 3.76% | 4.83% | 4.30% | 7.45% | 5.43% | 4.79% | 6.97% | 4.62%
vht 13 | 4.78% | 4.43% | 4.29% | 4.31% | 6.40% | 4.56% | 4.31% | 5.97% | 4.17%
vht 14 | 5.86% | 5.27% | 6.10% | 6.13% | 6.41% | 5.16% | 6.67% | 8.08% | 5.39%
vht 15 | 6.71% | 5.51% | 6.36% | 6.16% | 5.07% | 5.53% | 6.00% | 8.23% | 5.86%
vht 16 | 7.23% | 5.48% | 7.00% | 6.56% | 4.62% | 10.95% | 6.82% | 10.19% | 7.18%
vht 17 | 9.49% | 6.88% | 10.18% | 9.04% | 8.19% | 12.09% | 7.62% | 10.12% | 9.21%
vht 18 | 8.73% | 8.10% | 10.26% | 9.00% | 8.92% | 8.67% | 7.56% | 9.70% | 8.75%
vht 19 | 5.67% | 7.19% | 5.36% | 5.18% | 5.58% | 5.29% | 4.82% | 5.41% | 5.72%
vht 20 | 4.64% | 9.05% | 5.25% | 5.39% | 4.04% | 5.57% | 4.49% | 3.14% | 4.44%
vht 21 | 4.52% | 10.78% | 6.12% | 6.26% | 3.43% | 5.76% | 4.82% | 3.08% | 4.79%
vht 22 | 3.45% | 6.49% | 4.85% | 5.64% | 2.91% | 3.61% | 3.38% | 1.77% | 3.33%
vht 23 | 2.20% | 3.82% | 3.39% | 3.74% | 2.09% | 1.84% | 2.60% | 1.95% | 2.12%
Overall | 541.36 | 23.84 | 582.38 | 579.24 | 18.99 | 23.66 | 109.62 | 22.33 | 64.20
Table 3: The forecast table for Figure 9. Figure 9: Instances of the
prediction results (bright lines) calculated by our neural network are
compared with the ground truths (faded lines).  Figure 10: These circle areas
represent the values of the ground truth or the prediction of daily average
VHT taking place in nine samples (from left to right: ST82, ST271, ST279,
ST490, ST673, ST912, ST932, ST990, ST1077). Figure 11: Urban function density
map of the three different areas used in the accuracy test (2,3) and the
multi-area hybrid test (1,2,3). (1: Chengdu Urban Area, 2: Haikou Urban Area,
3: the Entire Haikou)

###  3.2 Cross-city Experiment

With the aid of trained computational models, we successfully analyzed and
judged the results of traffic demand in Haikou based on the given built
environment information. However, it remains to be verified if this prediction
model can be migrated across different regions. The twofold issue manifests as
such: (1) whether a predictive model trained in one city can be applied to
another city, and (2) whether a model trained in one part of a city can be
used for the entire area. To verify the universality of the relationship
between environment and vehicle travel demand and further demonstrate the
applicability of our method, we trained the neural networks for different
areas (Figure 11) and conducted a hybrid test. In addition, although ANNs
performed best in fitting the mapping relationship in the preliminary five-
fold accuracy verification test, algorithms such as RF and Linear SVR are also
reconsidered while accounting for the modification in training data and test
sets.

This section details the final accuracy of our prediction models in the
experiments involving both the urban and whole of Haikou, Chengdu urban area,
and algorithmic adjustments for better performance in multi-area applications.
Data sampling and model training processes were conducted on these different
areas, and the trained models are applied to the other two regions to acquire
accuracy. By comparing and analyzing the median accuracy of models applied to
different areas and graphing the prediction results, we determine the extent
to which the prediction model can be reused.

####  3.2.1 Aggregate Forecasting: Region Migration

First, on the topic of VHT aggregate forecasts used across regions, we applied
the model trained to the center of one city and then to that of another.
Secondly, the prediction values are visualized to show the effect of the
computational model and compare the experimental results of different
algorithms and datasets (Figure 12). The predicted total VHT for each sample
is remapped to the value before normalization and translated into a color
value. Brighter and warmer colors represent higher values. Sample color blocks
are reassembled in the area of interest and measured onto a map in km,
depicting the predicted total vehicle travel demand as a macroscopic
geographic distribution to be compared to reality. In addition, the relative
error between the predicted and true values for each sample is visualized as a
color value. Blacker means a higher accuracy of the prediction model whereas
whiter represents a higher error value. Similarly, the error value color block
is reallocated onto the map.

Figure 12: Accuracy and visualization of total vehicle travel demand in the
cross-city experiments.

One model is trained in Haikou, then tested in Chengdu, and vice versa with
the other (Figure 12). After comparing the prediction images generated by the
different algorithms and the median accuracy of the samples, we suggest that
ANNs bear the best prediction results. ANNs boast not only higher median
accuracy but also generate images with prediction values closest to the real
VHT distribution. The prediction results of RF have a low subdivision in the
color value of VHT aggregate. The degree of subdivision and accuracy increase
with the tree depth value, but the change becomes extremely subtle when depth
exceeds 5. The accuracy of SVM is also not as high as ANNs, and according to
the visualization of the predicted values, SVM overestimates the VHT value of
samples with sparse travel activity.

The visualization of VHT generated by ANNs matches the spatial distribution
trend of the real situation. To clarify, total VHT in Chengdu decreases from
the lower-left outward in the predicted image, and the total VHT in Haikou
aggregates laterally in the middle of the predicted map. This indicates that
the prediction model for the total VHT amount works for another city whose
data is completely unlearned by the artificial neural network during the
training process.

However, the color value distribution tends toward average more than the real.
We tested different activation functions to obtain better prediction results.
The experiments show that Relu and Softmax fail to fit the training of the
prediction model. The results of Tanh and Sigmoid are similar. We ultimately
chose Sigmoid as the final activation function because it provides a more even
distribution of color values while Tanh generates predictions with slightly
lower accuracy. Under those conditions, Sigmoid brightens the darker parts of
the upper left and lower right diagonal of Chengdu prediction. In the Haikou
prediction image, it darkens the central area (Figure 12).

Additionally, the accuracy images of ANNs show the sample with ground truths
of higher total VHT amount have darker color values (i.e., the ANNs model
holds a higher prediction accuracy on that sample). However, the sample with
the highest accuracy is not at the center of the city, where the highest VHT
values converge, but in the outer ring, where the total VHT values are
slightly lower. The black area in the accuracy plot consistently appears as a
ring, and the interior contains dark gray blocks slightly brighter compared to
the edges, meaning the accuracy of the ANN model first starts higher and
lowers from the center of the city outward.

In general, the prediction models for VHT aggregate are migratable between
different urban areas provided that ANNs use Sigmoid, possessing a median
accuracy of around 75%, increasing and then decreasing from the city center
outwards.

####  3.2.2 Aggregate Forecasting: Region Expansion

However, there exists the question of applying models trained in a small-scale
urban area dataset to a larger scale one. While we have achieved excellent
accuracy in the entire Haikou area in our previous accuracy experiments using
ANNs in a randomly distributed geographic sample, cross-regional experiments
using ANNs, RF, and SVM show extremely low accuracy when the models used for
Haikou are applied to a larger-scale citywide area (Table 4). This is due to
certain randomness in areas with sparse travel activity, a conclusion
strengthened by the accuracy visualizations (Figure 12). Samples with high
error rates, marked with bright colors, in the accuracy images are always
sparse in total VHT, which are shown as dark color in the ground truth images.
As previously stated in the accuracy experiments, it is difficult for the
algorithms to acquire irregular patterns from the data samples with sparse
travel activity. Valid mapping relationships in these remote areas acquired by
the algorithms in areas with dense travel activity are also a challenge. In
applying the model to an entire city area, the algorithm overfitting occurs
when exclusively trained on the urban center region.

Different computational models | Accuracy
---|---
ANNs | Haikou Urban | 17.21%
Chengdu Urban | 33.35%
Linear SVR | Haikou Urban | -65.69%
Chengdu Urban | -21.49%
Random Forest | Haikou Urban | -178.32%
Chengdu Urban | -269.16%
Table 4: This table records the accuracy of VHT amount predictions in entire Haikou area. | Model U→Set A | Model A→Set U
---|---|---
ANNs | 36.10% | 61.67%
Random Forest | 30.12% | 52.00%
Linear SVR | -65.68% | 15.91%
Table 5: The all-region data set is divided into two parts, one of which (A) has monthly VHT accumulation above 2000 hours on each sample, the other (U) has monthly VHT accumulation under 2000 hours on each sample. This table shows the prediction accuracy of three algorithms between the two data sets. Figure 13: Over-fitting of ANNs in the prediction of the total VHT amount. | Accuracy of temporal distribution in different test areas
---|---
Different computational models | Haikou All-region | Haikou Urban | Chengdu Urban
| Haikou All-region | - | 93.58% | 80.04%
| Haikou Urban | 83.47% | - | 77.28%
ANNs | Chengdu Urban | 77.05% | 80.43% | -
| Haikou All-region | - | 89.31% | 79.02%
| Haikou Urban | 82.95% | - | 75.79%
Random Forest | Chengdu Urban | 76.93% | 78.19% | -
| Haikou All-region | - | 91.34% | 78.22%
| Haikou Urban | 80.38% | - | 73.74%
Linear SVR | Chengdu Urban | 67.68% | 76.50% | -
Table 6: This table records the accuracy results of temporal distribution in
the cross-area experiment. Figure 14: The output instances of prediction model
which uses the same training set as figure 9 but is tested in Chengdu

Another experiment is conducted to illustrate the over-fitting phenomenon that
transpires in citywide areas. The all-region dataset once again is divided
into two parts. One contains monthly VHT accumulation above 2000 hours on each
sample. The other has a monthly VHT accumulation under 2000 hours. Using the
multi-area test method, we tested the capability of three algorithms trained
on one data set and then be applied on the other (Table 5). In reference to
ANNs, the model of artificial neural network over-fits in the early stage of
training according to the error rate curve (Figure 13), leading to the model
of ANNs having an extremely low accuracy.

####  3.2.3 Temporal Distribution Forecasting

For a detailed VHT temporal distribution, the median accuracy values obtained
in the experiments closely approach the expected (Table 6) in both test region
expansion and migration. The prediction accuracy of ANNs remains higher than
that of SVM and RF. This is true in all the multi-area tests. According to the
table 6, taking the multi-area test between urban areas of Chengdu and Haikou
for example, the median accuracy reaches about 80% when the computational
model is applied to a broader urban scale or the built environment of another
urban area. It means that the prediction model has applicability among
different urban environments, and the mapping relation acquired in one area
can be reused elsewhere to a certain degree. To better demonstrate the
accuracy of the prediction model’s versatility, we selected and visualized
three samples’ temporal distribution predictions using all of Haikou as the
training set and Chengdu environmental data as the input of the test process.
The predictions of these three samples have similar patterns and the accuracy
of them exceeds (85.54%), hovers around (81.22%), or strays below the median
(74.22%) Figure 14). Compared with the richer patterns in figure 9, the model
predicts a time distribution on these Chengdu samples close to real-time
situations.

We have verified a link between our living environment and vehicle travel
demand, which is consistent with previous studies of Cervero et al., and
attempted to apply this relationship learned by the computational model in one
area to a broader urban or other areas. Although there exist issues in
predicting the total amount of VHT when applying computational models from
congested city zones to fringes with scant activity, migration experiments
have demonstrated the potential of applying models trained in one area to
others. Thus, using this approach, the dataset from a limited number of areas
can be utilized to map the relationship between living environment and vehicle
travel demand. This will help urban researchers and designers in predicting
detailed travel demand and offer feedback on improvements in the living
environment, thus significantly reducing their upfront research costs.

###  3.3 AI-assisted Urban Design

In addition, we experimented with the final model results in terms of design
applications. The final hybrid prediction model consists of a combination of
ANNs with six and seven hidden layers for predicting, respectively, the amount
of vehicle travel demand and the related temporal distribution. As stated
previously, a large body of prior research showed that mixed land use can
promote non-vehicular traveling and thus reduce demand. However, the extent
and specific effects of functional mixing are vague in most studies. Without
such references, designers struggle to intuitively and quickly obtain feedback
on functional blends. To demonstrate the value of our approach in this
scenario, hybrid computational models are used in this study to explore a
novel design approach.

Figure 15: The output instances of prediction model which uses the same
training set as figure 9 but is tested in Chengdu Figure 16: the combination
of genetic algorithms and hybrid ANNs

We selected and adjusted the urban functions of the buffer areas near four
sample points: Nanda Overpass (110.3303, 20.0199), Haikou People’s Park
(110.3392, 20.0363), Haikou East Railway Station (110.3382, 19.9854), and
Hainan Government (110.3441, 20.0207). Four different types of urban functions
are focused in the study related to the choice of travel patterns:
restaurants, residential areas, workplaces, and public transportation. In
addition, we endeavored to predict the case where the original function ratios
are maintained while the density thereof increases, including where the number
of urban functions is the same for all types (i.e., the case where the entropy
of the urban function mix is highest). The table 7 records our inputs for
seven different functional ratios, including the original functional numbers,
at four locations. Simultaneously, the figure 15 records the outputs of our
hybrid computational model for predicting vehicle travel demand in these
modified living environments. Notably, the designer must interpret the results
as associative because the link between living environment and travel demand
learned by the computational model is not causal in nature.

The conclusion that can be drawn from the figure 15 is that a perfectly
average distribution of functions does not imply an optimal distribution of
traffic demand. It is a possibility that the largest entropy in a mix of urban
functions begets an even more irrational distribution of traffic demand. The
green line in the figure represents numerical uniformity in all the functions,
and peak traffic demand not only stagnates but indeed could increase. This
again illustrates the potential for misleading conclusions to be drawn from
vaguely describing information in the urban environment. Conversely, among the
adjustment measures for the four explicit functional categories, the approach
with a clear effect involves mainly public transport. A reduction of traffic
pressure in all four locations can be notably observed when public transport
stations increase. At the same time, an increase of residential functions for
the peak traffic periods depends on the situation. Adding residential block
modules near Haikou People’s Park corresponds to the relief of traffic
pressure. Similar measures performed near East Station instead correspond to
higher traffic demand.

| Nanda Overpass
---|---
POI | 00 | 01 | 02 | 03 | 04 | 05 | 06 | 07 | 08 | 09 | 10 | 11 | 12 | 13 | 14 | 15 | sum
1 | 88 | 19 | 10 | 18 | 72 | 103 | 112 | 3 | 122 | 44 | 108 | 71 | 0 | 27 | 90 | 16 | 904
2 | 88 | 35 | 26 | 34 | 88 | 119 | 112 | 3 | 122 | 44 | 108 | 71 | 0 | 27 | 90 | 16 | 984
3 | 88 | 19 | 10 | 18 | 72 | 103 | 112 | 3 | 202 | 44 | 108 | 71 | 0 | 27 | 90 | 16 | 984
4 | 88 | 19 | 10 | 18 | 72 | 103 | 112 | 3 | 122 | 84 | 128 | 91 | 0 | 27 | 90 | 16 | 984
5 | 88 | 19 | 10 | 18 | 72 | 103 | 112 | 3 | 122 | 44 | 108 | 71 | 0 | 107 | 90 | 16 | 984
6 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 984
7 | 95 | 20 | 10 | 19 | 78 | 112 | 121 | 3 | 133 | 47 | 117 | 77 | 0 | 29 | 97 | 17 | 984
| Haikou People’s Park
POI | 00 | 01 | 02 | 03 | 04 | 05 | 06 | 07 | 08 | 09 | 10 | 11 | 12 | 13 | 14 | 15 | sum
1 | 40 | 191 | 86 | 178 | 168 | 247 | 214 | 32 | 205 | 259 | 254 | 284 | 0 | 27 | 118 | 102 | 2405
2 | 40 | 207 | 102 | 194 | 184 | 263 | 214 | 32 | 205 | 259 | 254 | 284 | 0 | 27 | 118 | 102 | 2485
3 | 40 | 191 | 86 | 178 | 168 | 247 | 214 | 32 | 285 | 259 | 254 | 284 | 0 | 27 | 118 | 102 | 2485
4 | 40 | 191 | 86 | 178 | 168 | 247 | 214 | 32 | 205 | 299 | 274 | 304 | 0 | 27 | 118 | 102 | 2485
5 | 40 | 191 | 86 | 178 | 168 | 247 | 214 | 32 | 205 | 259 | 254 | 284 | 0 | 107 | 118 | 102 | 2485
6 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 2485
7 | 41 | 197 | 88 | 183 | 173 | 255 | 221 | 33 | 211 | 267 | 262 | 293 | 0 | 27 | 121 | 105 | 2485
| Haikou East Railway Station
POI | 00 | 01 | 02 | 03 | 04 | 05 | 06 | 07 | 08 | 09 | 10 | 11 | 12 | 13 | 14 | 15 | sum
1 | 82 | 498 | 157 | 488 | 50 | 74 | 106 | 5 | 106 | 102 | 41 | 114 | 2 | 15 | 23 | 13 | 1876
2 | 82 | 514 | 173 | 504 | 66 | 90 | 106 | 5 | 106 | 102 | 41 | 114 | 2 | 15 | 23 | 13 | 1956
3 | 82 | 498 | 157 | 488 | 50 | 74 | 106 | 5 | 186 | 102 | 41 | 114 | 2 | 15 | 23 | 13 | 1956
4 | 82 | 498 | 157 | 488 | 50 | 74 | 106 | 5 | 106 | 142 | 61 | 134 | 2 | 15 | 23 | 13 | 1956
5 | 82 | 498 | 157 | 488 | 50 | 74 | 106 | 5 | 106 | 102 | 41 | 114 | 2 | 95 | 23 | 13 | 1956
6 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 1956
7 | 85 | 519 | 163 | 508 | 52 | 77 | 110 | 5 | 110 | 106 | 42 | 118 | 2 | 15 | 23 | 13 | 1956
| Hainan Government
POI | 00 | 01 | 02 | 03 | 04 | 05 | 06 | 07 | 08 | 09 | 10 | 11 | 12 | 13 | 14 | 15 | sum
1 | 50 | 16 | 21 | 50 | 65 | 66 | 87 | 8 | 87 | 166 | 96 | 75 | 0 | 15 | 83 | 24 | 909
2 | 50 | 32 | 37 | 66 | 81 | 82 | 87 | 8 | 87 | 166 | 96 | 75 | 0 | 15 | 83 | 24 | 989
3 | 50 | 16 | 21 | 50 | 65 | 66 | 87 | 8 | 167 | 166 | 96 | 75 | 0 | 15 | 83 | 24 | 989
4 | 50 | 16 | 21 | 50 | 65 | 66 | 87 | 8 | 87 | 206 | 116 | 95 | 0 | 15 | 83 | 24 | 989
5 | 50 | 16 | 21 | 50 | 65 | 66 | 87 | 8 | 87 | 166 | 96 | 75 | 0 | 95 | 83 | 24 | 989
6 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 989
7 | 54 | 17 | 22 | 54 | 70 | 71 | 94 | 8 | 94 | 180 | 104 | 81 | 0 | 16 | 90 | 26 | 989
Table 7:  The input table for Figure 15. This table records the attempts of
urban function adjustment using the computational model under four urban
locations. (Columns 00 to 15 annotate the number of 16 urban function types in
Table 8 as input of each attempt; rows 1 to 7 indicate: numbers of original
functions, numbers after adjustment of shopping-related urban functions,
adjustment of residential areas, adjustment of workplace-related urban
functions, adjustment of public transportation-related urban functions such as
bus station, and the case making the number of all types of functions mutually
equal to each other, the case maintaining the original proportion of
functions)

###  3.4 Combined with Genetic Algorithm

Further, we embed the trained model into a genetic algorithm. The combination
of genetic algorithms and predictive models (Figure 16) help discover the
optimal or near-optimal solution for our living environment under a specific
travel distribution objective. Firstly, a group of urban functional ratios
that satisfy the initial constraints is created randomly. Individual objects
are entered into a hybrid ANNs model and output predictions, and further
evaluations are performed to derive a metric related to travel demand.
Referring to this metric as the fitness, the best group of individual objects
representing the built environment is selected and subsequently reassessed.
The cycle of evaluation, selection, and derivation operations repeats until an
optimal or near-optimal solution arrives.

Urban decision-makers can set the desired travel demand for a certain urban
area or period to solve problems such as the temporal distribution of
transportation resources, which might require optimizing the ratios of urban
functions within a range for a specific location. In a practical sense, that
involves mixing residential and office areas to construct self-contained
communities at the proper scale to solve the problem of traffic peaks in job
to home.

With some locations of interest, the functional ratios in the built
environment are used as individual objects for optimization by the genetic
algorithm under certain constraints and objectives set by the “designer.” The
predictive model or its variants are used as the fitness function to optimize
the functional ratios.

Using Haikou People’s Park as an example, the number of total urban function
interest points and type-12 (transportation hub) remain unchanged. The
remaining interest points vary up or down by 50 based on the current number,
and all are greater than or equal to 0. The objective function is to minimize
the variance of vehicle travel demand at different periods to optimize the
invocation of transportation resources and release the traffic pressure during
peak periods. After the iterative process of the genetic algorithm, the
designer can obtain the optimized results as shown in the figure 17 (light
blue line). Indeed, the peak of travel demand can be relieved with the new
urban function ratio.

Figure 17: The optimized result in Haikou People’s Park

In addition, although invoking the trained ANNs model for each prediction
takes 525 ms ± 6.41 ms, the genetic algorithm performs many operations, and
the process can take up to several hours. The designer can place additional
constraints to better fit the actual situation and significantly reduce the
optimization time of the genetic algorithm. For example, the designer can set
four further base parameters weighted to determine the amount of variation in
the four POI categories of eating, housing, work, and public transportation.
They can subsequently optimize these four parameters in the genetic algorithm
instead of optimizing all 16 POI quantity parameters to conserve time. The
figure 17 also contains the results of this more constrained optimization
(dark blue line), which also maps out a more reasonable travel demand time
variation with a lower peak than the original one (magenta line). The proposed
POI ratios (dark blue columns) derived from this approach are less modified
for the built environment. In the case demonstrated here, all functional
categories remain largely unchanged except for residential density, which
should be increased.

##  4 Conclusion and Further Study

Past research revealed an intrinsic link between land use and travel demand,
which helped designers consider urban functional planning from a new
perspective. However, there lacked a proper methodology allowing the ability
designers to obtain detailed travel demand predictions and clear
recommendations for urban planning adjustments. Also, the methodology of past
studies resulted in a lack of a sufficient basis to migrate findings between
different regions.

Meanwhile, the emergence of machine learning and big data created a new
paradigm of urban research, and several studies utilized machine learning
models to enhance traditional workflow and take a more detailed analysis and
prediction of traditional urban problems. Our research develops a new
computational model to map the relationship between built environments and
travel demand, thus creating a network of feedback in the urban planning and
design process. The study shows how computational models can be adapted and
trained to accurately predict the relationship between an array representing
the built environment and vehicle travel demand. The accuracy and
transferability of the computational models are further proven to be feasible.
With the trained model, we can accurately predict the vehicle travel demand
that may occur at a location at various times.

With this prediction model, designers can establish explicit urban function
compositions as the input and make predictions to obtain a detailed vehicle
travel demand profile including total amount and its time distribution. In
addition, we show separately in our application exploration how the designer
can adjust the inputs to obtain feedback and further leverage the genetic
algorithm to obtain explicit suggestions for city function adjustment with the
expected travel demand target. In addition, other factors such as socio-
economic and policy factors can also affect traffic behavior and travel demand
(Forsyth et al.,, 2008; McNally and Kulkarni,, 1997). In future studies, we
aim to quantify these behaviors.

##  5 Appendix

Index | Building Function |
---|---|---
00 | automobile and motorcycle related |
01 | food and beverages related |
02 | shopping related place |
03 | daily life service place |
04 | sports and recreation place |
05 | medical and health care service place |
06 | accommodation service related |
07 | tourist attraction related |
08 | residential area |
09 | enterprise |
10 | governmental and social groups related |
11 | science and education cultural place |
12 | traffic hinge |
13 | transit network |
14 | finance and insurance service institution |
15 | public facility |
Table 8: Urban Function Category According to AutoNavi

##

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