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Transportmetrica A: Transport Science
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/ttra21
Real-time traffic incident detection based on a
hybrid deep learning model
Linchao Li, Yi Lin, Bowen Du, Fan Yang & Bin Ran
To cite this article: Linchao Li, Yi Lin, Bowen Du, Fan Yang & Bin Ran (2020): Real-time traffic
incident detection based on a hybrid deep learning model, Transportmetrica A: Transport Science,
DOI: 10.1080/23249935.2020.1813214
To link to this article: https://doi.org/10.1080/23249935.2020.1813214
Published online: 06 Sep 2020.
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TRANSPORTMETRICA A: TRANSPORT SCIENCE
https://doi.org/10.1080/23249935.2020.1813214
Real-time traffic incident detection based on a hybrid deep
learning model
Linchao Lia,YiLin
b, Bowen Duc,FanYang
dand Bin Rand
aCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen, People’s Republic of China;
bCollege of Computer Science, Sichuan University, Chengdu, People’s Republic of China; cState Key
Laboratory of Software Development Environment, Beihang University, Beijing, People’s Republic of China;
dSchool of Transportation, Southeast University, Nanjing, People’s Republic of China
ABSTRACT
Small sample sizes and imbalanced datasets have been two diffi-
culties in previous traffic incident detection-related studies. More-
over, real-time characteristics of incident detection models must be
improved to satisfy the needs of traffic management. In this study,
a hybrid model is proposed to address the above problems. In the
proposed model, a generative adversarial network (GAN) is used to
expand the sample size and balance datasets, and a temporal and
spatially stacked autoencoder (TSSAE) is used to extract temporal
and spatial correlations of traffic flow and detect incidents. Using
a real-world dataset, the model is evaluated from different aspects.
The results show that the proposed model, considering both tem-
poral and spatial variables, outperforms some benchmark models.
The model can both increase the incident sample size and balance
the dataset. Furthermore, the sample selection method improves the
real-time capacity of the detection.
ARTICLE HISTORY
Received 26 April 2019
Accepted 9 August 2020
KEYWORDS
Generative adversarial
networks; deep learning;
autoencoder; small sample
size; imbalanced data
1. Introduction
Traffic congestion is a major concern for many large cities worldwide. For example, com-
muters lose over 100 hours per year because of traffic congestion in Los Angeles; in the
UK, traffic congestion cost motorists over 49.7 billion dollars in 2017 (Cookson 2018). Traffic
incidents can cause traffic congestion, which reduces highway capacity, increases the prob-
ability of a second crash, and increases air pollution. Therefore, traffic incident detection is
important so that traffic managers can respond and manage incidents and so that travelers
can select the best route to reduce travel time.
The emergence of the Intelligent Transportation System (ITS) concept provides strong
support for traffic incident detection. In particular, massive numbers of sensors in the sys-
tem, such as loop detectors, can collect large amounts of traffic flow data. The detectors can
measure changes in the traffic flow near an incident and transmit the data to the system (Li
et al. 2018). For example, when an incident occurs, the traffic volume of downstream and
upstream detectors may change. In other words, collecting this large volume of traffic data
CONTACT Yi Lin yilin@scu.edu.cn
© 2020 Hong Kong Society for Transportation Studies Limited
2L. LI ET AL.
is fundamental to a successful incident detection model, and mining the patterns of traffic
flow data effectively is highly important.
However, the datasets used to train such models still face two large challenges. The first
challenge is the collection of sufficient incident samples, which are difficult to extract from
the database – especially for some newly built highways – because the total number of inci-
dent samples is low. A small sample size may negatively affect the training of the incident
detection model. The second is imbalanced samples. When we extract samples, far more
non-incident samples can be obtained than incident samples, which causes some model
training difficulties for machine learning-based incident detection models.
Advanced machine learning algorithms provide methodological support for traffic inci-
dent detection, which can be defined as a binary classification problem. In traffic incident
classification, an incident sample can be defined as a 1, while a non-incident sample can
be defined as a 0. The goal is to train an algorithm that can classify newly acquired sam-
ples. Compared with traditional statistical methods, machine learning methods, especially
deep learning methods, have some advantages because they are able to extract traffic
information effectively and efficiently from raw mixed data.
Based on deep learning methods, in this study, a hybrid model coupling a generative
adversarial network (GAN) and a temporal and spatially stacked autoencoder (TSSAE) is
developed to solve the above problems. The main contributions of this study are as follows.
•The GAN is applied to solve the small sample size and imbalance problems of traffic inci-
dent datasets. This approach can increase both the number of incident samples and their
diversity, which improves the performance of the incident detection model.
•Temporal and spatial variable selection rules are proposed that are useful for capturing
the temporal and spatial patterns of traffic flow. Using these rules, the incident detection
model can extract the important features that different incidents and non-incidents.
•A temporal and spatial incident detection model is developed that can mine deep fea-
tures in the traffic flow data. Moreover, the samples selected to train the model improve
its real-time characteristics.
•The proposed hybrid model is evaluated from different aspects using a real-world
dataset. The results indicate that our new model both increases the accuracy and
improves the real-time characteristics of incident detection.
The remainder of this paper is organized as follows. In Section 2, some previous stud-
ies regarding incident detection and remaining problems are presented. In Section 3,we
introduce our proposed model. The data used in this study are described in Section 4,and
Section 5provides an analysis of the results. Finally, Section 6concludes the study and
suggests avenues for future work.
2. Literature review
Traffic incident detection model is a popular topic in previous studies. In general, the
applied models can be divided into two categories: statistics-based models and machine
learning-based models (Ghosh and Smith 2014). Each model type is briefly discussed
below.
TRANSPORTMETRICA A: TRANSPORT SCIENCE 3
•Statistics-based models: These types of models test differences in traffic flows based on
statistical techniques, where a significant difference indicates a possible incident. The
popular California and McMaster algorithms are representative of this type of model
and have been widely applied (Hall, Shi, and Atala 1993; Samant and Adeli 2000).
However, these simple models cannot provide sufficient accuracy to meet the require-
ments of an Intelligent Transportation System (Samant and Adeli 2000). To cap-
ture the temporal and spatial correlations among traffic flows, some studies imple-
mented advanced statistical techniques. For example, an autoregressive integrated
moving average model was built to detect traffic incidents on the Lodge High-
way in Detroit (Ahmed and Cook 1979); the proposed detection logic performed
smoothing using a moving average filter and obtained better results (Chassiakos
and Stephanedes 1993). Later, a multiple model particle smoother was introduced
to convert the incident detection problem into a traffic state prediction problem
and solve it effectively (Wang, Fan, and Work 2016; Wang, Work, and Sowers 2016).
Although statistics-based models have been widely applied, they have some short-
comings. First, the algorithm assumptions may not be consistent with the actual
traffic flow data. Second, these models are highly dependent on user experience.
When implementing a statistics-based model, the thresholds are often set manu-
ally by the users. Moreover, these models sometimes cannot simultaneously consider
the temporal and spatial correlations among traffic flow data (Mak and Fan 2007;Li
et al. 2019).
•Machine learning-based models: To make the incident detection model more flexible
and robust, various machine learning models have been applied. Because the models
are driven by the data, such models can easily be implemented without specialized
knowledge. The traffic incident detection problem is first converted into a binary clas-
sification task in which an incident is defined as a 1 and a non-incident is defined as
a 0. Then, a machine learning model such as a support vector machine (SVM) (Yuan
and Long Cheu 2003; Chen, Wang, and Van Zuylen 2009;XiaoandLiu2012), classifica-
tion tree (CT) (Chen and Wang 2009), random forest (RF) (Liu, Lu, and Chen 2013), or
artificial neural network (ANN) (Samant and Adeli 2001; Adeli and Samant 2000)can
be used to solve the task. Li et al. compared some famous machine learning mod-
els and found that ensemble approaches improve the performance. Adding a bagging
strategy to an SVM increased the accuracy (Li et al. 2016b). Some advanced ANN mod-
els have been widely applied in previous traffic incident detection studies and have
obtained better results. As deep learning theory has developed, some models have
already been applied in various transportation areas. Ma et al. used a deep neural net-
work to recognize traffic congestion on a highway network using both temporal and
spatial traffic flow characteristics (Ma et al. 2015). Zhu et al. developed an incident
detection model at the network level based on a convolutional neural network (CNN)
(Zhu et al. 2018). It has been proven that deep learning models outperform traditional
machine learning models because they can fully mine the traffic information from the
data. However, achieving a sufficient number of samples is difficult when applying a
deep learning model. Consequently, simulated data have been widely used, but some-
times such data does not represent the true highway traffic flow (Lv et al. 2015;Ma
et al. 2017; Zhu et al. 2018;Wuetal.2018). Another method applied to solve the small
sample size problem is to collect samples during each incident as incident samples to
4L. LI ET AL.
increase the sample size. However, this approach could affect the real-time capacity of
the model.
Thus, the previous studies exhibit some problem that still need to be solved, including
the following:
•How to obtain a richer set of traffic incident samples to train and test the model;
•How to construct a balanced dataset in which the number of incident samples equals
the number of non-incident samples;
•How to improve the real-time capability of a traffic incident detection model;
•How to effectively extract the spatial and temporal correlations from the traffic flow data
to improve the performance of a traffic incident detection model.
To fill the above gaps, we apply deep learning theory in our study. First, we use GANs to
solve the sample size problem. GANs are recent models in the deep learning area that were
proposed to mimic a data distribution to create new data similar to the original data. In
recent years, GANs have commonly been used to improve image processing capacity (Rad-
ford, Metz, and Chintala 2015), generate high-quality images (Mirza and Osindero 2014;
Denton, Chintala, and Fergus 2015; Odena, Olah, and Shlens 2016), and address text-to-
image tasks (Reed et al. 2016). In the transportation area, GANs have been applied to
detect driver behaviors (Ghosh, Bhattacharya, and Chowdhury 2016), in autonomous driv-
ing (Kuefler et al. 2017) and for traffic state estimation (Liang et al. 2018). To the best of
our knowledge, this study is the first attempt to solve the sample size problem for traffic
incident detection.
3. Methodologies
This section first introduces the GAN applied to increase the sample size of incident cases;
then, the TSSAE is implemented to detect traffic incidents. Finally, the novel hybrid model
developed in this study is presented.
3.1. Generative adversarial network
The commonly applied GAN model contains two parts: a generator G(z;θg), which is used to
generate new samples G(z)∈Rdfrom a random prior z∈Rrand a discriminator D(x;θd),
which is used to recognize whether a newly generated sample is real or fake. The goal is
to train a generative model Gthat can maximize the probability that the discriminative
model Dwill misclassify generated samples as real samples. As demonstrated by Good-
fellow et al. (2014), the GAN framework can be abstracted as a simple two-player minimax
game that completes when Nash equilibrium is satisfied. Thus, the objective of a GAN is to
minimize the following objective function:
min
Gmax
DV(G,D)=Ex∼pdata[log D(x)]+Ez∼pz[log(1−D(G(z)))](1)
where pdata and pzrepresent the distribution of the real sample and a random prior dis-
tribution (such as a Gaussian distribution), respectively. During the training process, the
TRANSPORTMETRICA A: TRANSPORT SCIENCE 5
parameters of Gand Dare updated using the following two equations:
θd←θd+α∇θd
1
m
m
i=1
(log D(xi)+log(1−D(G(zi))) (2)
θg←θg−α∇θg
1
m
m
i=1
log(1−D(G(zi)) (3)
where mrepresents the number of training samples and αis the step size. As demon-
strated by Goodfellow et al., the parameters of the generator Gare optimized by maximizing
log(D(G(z))) to speed up GAN training. Thus, Equation (3) is rewritten as:
θg←θg+α∇θg
1
m
m
i=1
log(G(zi)(4)
In this study, an alternative training method is applied that involves two steps. In the first
step, the generator Gis fixed, and the discriminator Dis optimized to maximize its accu-
racy. In the second step, the discriminator Dis fixed, and the generator Gis optimized by
minimizing the accuracy of the discriminator D. When pdata =pz, the training process is
terminated.
The architecture of a GAN is shown in Figure 1; the two models can be any type of mul-
tilayer perceptron. In this study, two fully connected neural networks are applied as the
generator and discriminator. The training procedures for a GAN are shown in Figure 2.
Figure 1. Architecture of the applied GANs.
6L. LI ET AL.
Figure 2. GAN training procedures.
3.2. Temporal and spatial stacked autoencoder (TSSAE)
3.2.1. Sparse autoencoder
An autoencoder (AE) is developed to extract latent features from raw data and then to
reconstruct the raw data based on the latent features. The data reconstruction makes the
AE extract deep hidden features to adequately represent the raw data. To recognize the
occurrence of a traffic incident, it is necessary to mine the hidden spatial and temporal fea-
turesoftrafficflows.AsshowninFigure3, an AE comprises an encoder En(x,θen)to extract
features that represent the input data xand a decoder De(En(x),θde)that reconstructs the
represented features to recreate the original input data x:
y=sig(W1x+b1)(5)
x=sig(W2y+b2)(6)
where xis the reconstruction of x;θen =(W1,b1)andθde =(W2,b2) are parameters of
the encoder and decoder, respectively, in which W1,W2are weight matrices and b1,b2
are biases. sig(·)represents the logistic sigmoid function (1+exp(−x))−1which is widely
applied in traffic flow prediction.
The objective of an AE is to minimize the error between the input data and the recon-
structed input data:
min 1
m
m
i=0
x−x2
2(7)
where mis the number of training samples. In our study, the goal is to extract the deep
hidden features of spatial and temporal variables. Therefore, a sparsity constraint is added
to the objective function to control the nonlinear mapping; then, the objective is rewritten
TRANSPORTMETRICA A: TRANSPORT SCIENCE 7
Figure 3. Architecture of the AE.
as follows:
min 1
mm
i=0
x−x2
2+γ
N
j=1
KL(ρ|| ˆρj)(8)
where σrepresents the weight of the sparsity constraint; Nis the number of variables; ρ
is the sparsity parameter to control the feature set; ˆρj=1
mm
i=1(yj)iis the average activa-
tion of the jth hidden unit ajover the mtraining samples; and KL is the Kullback–Leibler
divergence, which is given by:
KL(ρ|| ˆρj)=ρlog ρ
ˆρj
+(1−ρ)log 1−ρ
1−ˆρj
(9)
3.2.2. Temporal variable selection rules
As demonstrated in Abdel-Aty et al. (2004), Hossain and Muromachi (2012), Xu, Wang, and
Liu (2013) and Yu et al. (2016), finding the temporal correlations of traffic flow is essen-
tial when building a traffic incident detection model. Therefore, extracting the difference
between normal traffic conditions and risky traffic conditions is critical. The detectors widely
used in the current Intelligent Transportation System sense traffic flow data every 30 s,
including traffic speed, traffic volume and traffic density. In this study, we adopt the three
traffic flow parameters 5 min before an incident because many previous studies have shown
that traffic flow conditions start to deviate 5 min before an incident (Qu et al. 2017;Xu
et al. 2015). Moreover, the means and standard deviations of the traffic flow parameters
during this period are also calculated and selected as temporal variables. Finally, from each
detector, 3 ×10 +3×2=36 variables can be selected as temporal variables.
3.2.3. Spatial variable selection rules
Knowing the spatial correlations of traffic flow is also important to the incident detection
model. Based on shock wave theory, it can be inferred that some time must elapse for the
influence of an incident to spread. Thus, traffic flow parameters obtained from adjacent
upstream and downstream detectors should also be considered because traffic flow near
an incident is more sensitive than is more distant traffic flow. The traffic flow parameters
of upstream or downstream detectors change earlier; therefore, considering these vari-
ables can help the model detect incidents with less delay. For this study, we also selected
combinations of the traffic flow parameters obtained from the upstream and downstream
detectors as spatial variables. The combinations are shown to contribute to the detection
8L. LI ET AL.
model accuracy, such as the California algorithms that apply the difference between the
occupancy at two adjacent detectors as one of the variables (Karim and Adeli 2002).
3.2.4. TSSAE
After the selection, a total of 81 variables (listed in Table 1) are considered in the traffic
incident detection model. To deeply mine the correlations among the temporal and spatial
variables, the hierarchical model TSSAE is built, as shown in Figure 4. In the bottom layers,
the variables of different detectors are input to different sparse AEs. Then, the latent tempo-
ral features extracted by the different detectors are combined by an added joint layer that
learns the spatial correlations. The proposed model uses 78 variables are used rather than
the three combined spatial variables because the model can capture the spatial correlation
in the joint layer. After the high-level spatial and temporal feature learning, an output layer
is added consisting of a softmax classifier in this study, which is a supervised model whose
function is:
fout =1
1+exp(−W3z)(10)
where W3represents the weights and zrepresents the learned features.
The deep neural network can easily be trained by applying the backpropagation method
and the gradient-based optimization algorithm; however, it has been shown that deep
architectures trained in this way perform worse. Fortunately, Hinton et al. proposed a
greedy layer-wise unsupervised learning technique that can successfully optimize deep
neural networks (Hinton, Osindero, and Teh 2006; Bengio et al. 2007). First, the model is
Tab le 1. Variables selected using the proposed temporal and spatial rules (Li et al. 2020).
Variable Name
Speed at the upstream detector just after the incident s_up_0
Volume at the upstream detector just after the incident v_up_0
Occupancy at the upstream detector just after the incident o_up_0
Speed at the downstream detector just after the incident s_dn_0
Volume at the downstream detector just after the incident v_dn_0
Occupancy at the downstream detector just after the incident o_dn_0
Speed difference between the upstream and downstream detectors just after the incident s_up_dn
Volume difference between the upstream and downstream detectors just after the incident v_up_dn
Difference in occupancy between the upstream and downstream detectors just after the incident o_up_dn
Speed at the upstream detector t before the incident s_up_t
Volume at the upstream detector t before the incident v_up_t
Occupancy at the upstream detector t before the incident o_up_t
Speed at the downstream detector t before the incident s_dn_t
Volume at the downstream detector t before the incident v_dn_t
Occupancy at the downstream detector t before the incident o_dn_t
Mean upstream traffic speed during the 5 min before the incident m_s_up
Mean downstream traffic speed during the 5 min before the incident m_s_dn
Mean upstream traffic volume during the 5 min before the incident m_v_up
Mean downstream traffic volume during the 5 min before the incident m_v_dn
Mean upstream occupancy during the 5 min before the incident m_o_up
Mean downstream occupancy during the 5 min before the incident m_o_dn
Standard deviation of the upstream traffic speed during the 5 min before the incident s_s_up
Standard deviation of the downstream traffic speed during the 5 min before the incident s_s_dn
Standard deviation of the upstream traffic volume during the 5 min before the incident s_v_up
Standard deviation of the downstream traffic volume during the 5 min before the incident s_v_dn
Standard deviation of the upstream occupancy during the 5 min before the incident s_o_up
Standard deviation of the downstream occupancy during the 5 min before the incident s_o_dn
Note: In the table, t equals: 30 s, 60 s, 90 s, 120 s, 150 s, 180 s, 210 s, 240 s, 270 s, 300 s.
TRANSPORTMETRICA A: TRANSPORT SCIENCE 9
Figure 4. Architecture of the proposed TSSAE.
Tab le 2. Proposed TSSAE training procedures.
Training TSSAE
Given the training samples and the number of hidden layers, hidden modes,
Step (1) Pretrain
(1) Set the parameters for the objective function, including the weight of the sparsity constraint σand the sparsity
parameter ρ.
(2) Initialize the network parameters randomly.
(3) Conduct greedy layer-wise network training.
(a) Input the training samples to the first hidden layer, whose output forms the input of the next hidden layer.
(b) Optimize the parameters of the second layer by minimizing the objective function.
(c) Repeat (a) and (b) until the last hidden layer is reached.
Step (2) Fine-tuning
(1) Initialize the weights of the output layer.
(2) Fix the left temporal sparse AEs and apply backpropagation method and gradient-based optimization to tune
the network parameters.
(3) Fix the right spatial sparse AEs and apply backpropagation method and gradient-based optimization to tune the
network parameters.
pretrained in a bottom-up direction. Then, the parameters of the model are tuned using
backpropagation in a top-down direction. The procedural details are listed in Table 2.
3.3. The developed hybrid model
The preceding sections introduced the two key parts of our hybrid model, the GAN and
the TSSAE. The architecture of the proposed hybrid model is shown in Figure 5.TheGAN
is first applied to generate new incident samples using the selected spatial and temporal
variables. Then, the new datasets containing newly generated incident samples are used as
the input to the TSSAE. The last step is to evaluate the performance of the proposed model.
In this study, we apply four criteria: detection rate (DR), false alarm rate (FAR), classification
10 L. LI ET AL.
Figure 5. Architecture of the proposed hybrid model.
rate (CR) and the area under the curve (AUC).
DR =Number of incidents correctly detected
Number of actual incidents (11)
FAR =Number of incidents falsely detected
Number of the samples correctly detected (12)
CR =Number of samples correctly detected
Number of samples (13)
DR indicates the proportion of incidents correctly detected. A higher DR represents a more
accurate model. However, a model with higher DR may also be overly sensitive, that is,
it falsely detects more incidents (Asakura et al. 2017). Therefore, another criterion, FAR,
is introduced to evaluate model accuracy. AUC is the area under the receiver operating
characteristic (ROC) curve, which represents the classification ability of the model as the
discrimination threshold varies. Moreover, the computation time of the model is calculated
to evaluate its efficiency.
TRANSPORTMETRICA A: TRANSPORT SCIENCE 11
4. Data description
The first dataset used in this study was collected from a well-known, open traffic flow
data website called Caltrans Performance Measurement (PeMS), where we extracted the
incidents reported on I-80 in the US state of California from the incident database. Because
this study aims to model the relation between traffic flow and traffic incidents, incidents
that occurred in work zone areas were deleted. Second, traffic flow data measured by loop
detectors were obtained using the clearinghouse tool. Loop detectors are installed on the
highway approximately every 0.5 mile. We used the traffic flow parameters (including traffic
speed, volume and density) from more than 50 detectors. In addition, we calculated some
introduced combined variables. The above two datasets both include position variables
that can be used to join them together. We conducted this task using geographic informa-
tion system software. After combining the datasets, we found that some traffic flow data
corresponding to incidents were missing. To ensure the data quality, the samples with miss-
ing values were deleted. Finally, we obtained 139 complete incident samples and adopted
these data as the incident dataset.
Selecting non-incident samples corresponding to the incident samples is important but
difficult because it is impossible to guarantee that all conditions, such as weather con-
ditions, are the same. To eliminate the influences of other factors, we implemented the
commonly applied case control method (Abdel-Aty et al. 2004). We collected non-incident
samples under similar weather conditions at the same location during the same period
as the incident samples. Using this approach, several matched non-incident samples were
obtained for each incident sample. After selection, we obtained a total of 834 non-incident
samples and defined these data as the non-incident dataset.
The incident samples in the incident dataset are those that occurred just after the inci-
dent. Although the traffic flow of an incident sample is different from the traffic flow when
the incident happens, they are still quite similar. Furthermore, we selected some incident
samples with durations greater than 120 s. The samples that represent 30 s, 60 s, 90 s, and
120,s after these incidents, are extracted as incident samples and are defined as the 30-s-
incident dataset, 60-s-incident dataset, 90-s-incident dataset, and 120-s-incident dataset,
respectively. We want to build a real-time traffic incident detection model that can detect
an incident immediately after it happens; however, these samples can confuse the model
because the traffic flow during this period and the normal period differ more widely. The
real-time characteristics of our model are one of the main contributions of this study.
To set the parameters of the incident detection models, we conducted ten-fold cross-
validation. In this method, the dataset is first divided into ten parts, each with an equal
number of samples. Subsequently, nine parts are used to train the model, and the remaining
part is used to validate the model. This cross-validation process is repeated 10 times, and
each of the ten parts is used once as the validation set. Finally, the average error of the ten
cross-validations is calculated as the true error.
5. Results
5.1. A comparison of real and generated incident samples
Using the GAN, each raw sample is regenerated six times, creating a total of 834 inci-
dent samples. This dataset is defined as the generated-incident dataset. To evaluate the
12 L. LI ET AL.
Figure 6. Comparison of the statistics for the real and generated data (Lin et al. 2020). (a) Descriptive
statistics of the real data. (b) Descriptive statistics of the generated data.
performance of the GAN, the new incident samples are compared with the raw incident
samples. The resulting descriptive statistics, including the minimum, first quartile, median,
third quartile and maximum of the variables, are shown in Figure 6. Because each sample
includes so many variables, we selected 28 important variables (ranked by a random forest
model) to display in the figure, which shows that all five statistics of the newly generated
incident sample and raw incident sample variables are similar – but not the same. These
results indicate that the GAN can effectively generate incident samples. Moreover, the gen-
erated incident samples improve the sample diversity, which can contribute to improving
the accuracy of the incident detection model.
TRANSPORTMETRICA A: TRANSPORT SCIENCE 13
Figure 7. Differences in the correlations of variables between the real dataset and the generated
dataset.
To further analyze the effectiveness of the GAN, we calculated the difference between
the correlation matrix of the raw dataset and the correlation matrix of the generated
dataset, as shown in Figure 7. The difference is close to 0, which means that the correla-
tions between the variables in the raw dataset and the correlations between the variables
in the generated dataset are similar. This result indicates that the GAN captures the corre-
lations between variables in the raw samples and reflects those the correlations into the
generated samples, again indicating the effectiveness of the GAN model.
5.2. GAN eectiveness
The GAN was used in the proposed model to solve imbalance and sample size problems
when building a traffic incident detection model. Therefore, we conducted two experi-
ments. The first experiment evaluates the effectiveness of the GAN in dealing with the
imbalanced sample problem. In this experiment, the six different datasets shown in Table 3
are used to train the proposed model: five are imbalanced datasets, and one is a balanced
dataset. The incident samples are from the defined incident dataset, and the non-incident
14 L. LI ET AL.
Tab le 3. Descriptions of the datasets used in the experiments.
Data set Number of incident samples Number of non-incident samples
I1 139 139
I2 139 278
I3 139 417
I4 139 556
I5 139 695
I6 139 834
B1 139 139
B2 278 278
B3 417 417
B4 556 556
B5 695 695
B6 834 834
samples are taken from the defined non-incident dataset. The second experiment is
conducted to evaluate the effectiveness of the GAN in addressing the sample size prob-
lem. In this experiment, the six balanced datasets shown in Table 3are used to train the
proposed model. In contrast to experiment 1, this experiment uses the incident samples in
the generated-incident dataset.
When the imbalanced datasets are used to train the model, the model performance
tends to decrease as the ratio of non-incident samples and incident samples increases. From
Figure 8, it can be seen that the CR and FAR scores of the models trained by datasets I1,
I2, I3, I4, I5, and I6 tend to increase, while their DR and AUC results tend to decrease. This
indicates that imbalanced samples negatively affect the accuracy of the proposed incident
detection model. The results of models trained on the balanced datasets and the corre-
sponding imbalanced datasets can be found by comparing Figures 8and 9, which shows
that a model trained on a balanced dataset performs better than do models trained on the
imbalanced datasets. The average DR and AUC results of the six models using balanced
datasets decreased by 1.89% and 10.34%, respectively. Moreover, the balanced datasets
reduce the FAR by approximately 84.60% on average. This indicates that the generated
samples effectively improve the performance of the proposed incident detection model.
A comparison of the models trained on the balanced datasets shows that as the num-
ber of training samples increases, the performance of the models improves. Compared to
the dataset B1, the model trained on dataset B6, (a fivefold increase in the number of train-
ing samples) increases the DR, CR, and AUC by approximately 1.85%, 1.07%, and 8.24%,
respectively, and decreases the FAR by approximately 23.18%. The results indicate that the
GAN-generated additional samples can be used to train a more accurate incident detection
model.
5.3. The eectiveness of the TSSAE
In the hybrid model, the proposed TSSAE is applied as the traffic incident detection model.
To evaluate the performance of the TSSAE, several commonly used models, including BPNN,
SVM and RF, are implemented as benchmark models. To ensure fairness, both spatial and
temporal variables are considered in the benchmark models. Moreover, we compared the
newly built model using normal stacked AEs while considering spatial variables (SSAE),
temporal variables (TSAE), or both temporal and spatial variables (TSSAE).
TRANSPORTMETRICA A: TRANSPORT SCIENCE 15
Figure 8. Description and results of experiment 1.
Figure 9. Description and results of experiment 2.
In the BPNN model, two parameters need to be set: the number of hidden layers and the
number of hidden nodes in each hidden layer. In previous studies, it has been proven that
one hidden layer is sufficient (Sheela and Deepa 2013). The number of hidden nodes in the
hidden layer was set according to Sheela and Deepa (2013):
Hn=4n2+3
n2−8(14)
where nrepresents the number of input variables, which equals 81. To conduct SVM, two
parameters need to be set: gamma and soft margin C. Similar to the grid search method in
Li et al. (2016a), these two parameters are set as 0.0625 and 16, respectively. In RF, only the
16 L. LI ET AL.
Tab le 4. A comparison of models trained on balanced samples with models trained on imbalanced
samples.
Balanced samples Imbalanced samples
Model DR FAR CR AUC DR FAR CR AUC
TSSAE 0.9064 0.0520 0.8992 0.8518 0.8935 0.1005 0.9249 0.7727
SVM 0.8682 0.0689 0.8771 0.8399 0.8627 0.0701 0.9247 0.7883
RF 0.8527 0.0722 0.8589 0.8332 0.8432 0.1134 0.9093 0.7639
BPNN 0.8456 0.0791 0.8503 0.8293 0.8399 0.1204 0.9011 0.7593
California 0.6898 0.0930 0.7394 0.6602 0.6549 0.1495 0.7387 0.7290
McMaster 0.6904 0.1237 0.7459 0.6836 0.6893 0.1239 0.7529 0.6859
SSAE 0.7083 0.1599 0.7340 0.7829 0.6693 0.1763 0.7993 0.7482
TSAE 0.8502 0.0599 0.8755 0.8403 0.8493 0.1127 0.9089 0.7682
number of trees needs to be set. After some calculations, 100 trees was selected because
the accuracy does not increase after the number of trees reaches 100. When implementing
the ANN and SVM, the variables need to be normalized, but the RF uses raw variables. The
implementations of the two statistical models, California and McMaster, can be found in
Karim and Adeli (2002) and Hall, Shi, and Atala (1993) which provide detailed parameter
settings.
For SSAE, the 9 spatial variables in Table 1were used as input. A single layer with 5 hid-
den nodes is sufficient. For TSAE, we used the 78 temporal variables listed in Table 1.The
number of hidden layers was set to 3 with 39, 20, and 10 hidden nodes. For TSSAE, all the
variables in Table 1were used as input, and the parameters were the same as those in the
TSAE. The performances of the proposed detection models and the previous commonly
used models trained on imbalanced samples and balanced samples are listed in Table 4.
On the imbalanced and balanced datasets, the ratios of incident samples and non-incident
samples are 139:695 and 139:139, respectively and shows that our proposed model signifi-
cantly outperforms the benchmark models on most of the criteria. The results indicate that
the proposed TSSAE obtain its best performance on balanced samples but still achieves
better performances than other models on imbalanced samples. In our proposed hybrid
model, the imbalanced samples can be balanced; therefore, the hybrid model obtains the
best performance.
5.4. Real-time analysis of the hybrid model
In previous studies, models were built and tested using data acquired during incidents
(Chen, Wang, and Van Zuylen 2009; Chen and Wang 2009). For example, suppose the inci-
dent duration time is 5 min, traffic flow data can be obtained every 30 s from the detectors;
then, all 10 samples collected during those 5 min are used as incident samples. However, in
practice, the system should detect the incident as quickly as possible. Therefore, only the
sample taken closest to the time when an incident happens would be used as the incident
sample. In this study, to confirm this idea, we tested the proposed model using only the
dataset collect just after an incident occurred (I-1) and the constructed 30-s incident dataset
(I-2), 60-s incident dataset (I-3), 90-s incident dataset (I-4) and 120-s incident dataset (I-5).
The corresponding non-incident samples (NI-1, NI-2, NI-3, NI-4, NI-5) were selected from
the non-incident dataset. The training samples and test samples of the models are listed in
Table 5.
TRANSPORTMETRICA A: TRANSPORT SCIENCE 17
Tab le 5. Model training and test samples.
Model Training samples Test samples DR FAR CR AUC
TSSAE-1 70% of I-1, NI-1 30% of I-1, NI-1 0.9064 0.0520 0.8992 0.8518
TSSAE-2 70% of I-1, NI-1, I-2, NI-2 30% of I-1, NI-1 0.8923 0.0651 0.8901 0.8382
TSSAE-3 70% of I-1, NI-1, I-3, NI-3 30% of I-1, NI-1 0.8811 0.0819 0.8852 0.8218
TSSAE-4 70% of I-1, NI-1, I-4, NI-4 30% of I-1, NI-1 0.8602 0.0993 0.8529 0.8066
TSSAE-5 70% of I-1, NI-1, I-5, NI-5 30% of I-1, NI-1 0.8524 0.0992 0.8371 0.8012
Figure 10. Analysis of the effect of the time window.
As Table 5shows, the TSSAE-1 model obtains the best detection result, with DR, FAR, CR
and AUC values of 90.64%, 5.20%, 89.92% and 0.8518, respectively, while the TSSAE-5 model
obtains the worst detection result, with DR, FAR, CR and AUC values of 85.24%, 9.92%,
83.71% and 0.8012, respectively. The results show that except for FAR, the results of the
detection model decrease steadily from TSSAE-1 to TSSAE-5. This result occurs because as
the time when the incident samples obtained post-incident increases, the traffic flow differs
more dramatically. Thus, the trained model can accurately classify these incident samples
and non-incident samples, but it is not as sensitive to incident samples collected close to the
time when an incident occurs. The results indicate that using samples collected just after
incidents to train the proposed incident detection model can improve it, giving it strong
real-time capacity and good performance. The proposed model was trained using input
samples collected at different times to analyze the effect of the time window on model
accuracy. The time window means the time ahead of the incident. For 5, 10, 15, 20, 25, and
30 min ahead of an incident, the number of samples are 10, 20, 30, 40, 50, and 60, respec-
tively. The results are shown in Figure 10. As the time window of the input becomes longer,
the accuracy of the model increases slightly, but the computation time increases markedly.
6. Conclusion
Traffic incident detection is an important part of a traffic monitoring system. Incident detec-
tion can help practitioners create management plans that improve traffic safety and can
help travelers select the best travel routes to avoid congestion. However, incident samples
18 L. LI ET AL.
are difficult to collect, which stifles research and innovation. Moreover, achieving real-time
capability in an incident model is also difficult. In this study, to solve these problems, a
hybrid model coupling a GAN and a TSSAE is proposed. Using a real-world dataset extracted
from I-80 in California, the model is evaluated from several aspects.
The results indicate that our proposed model increases the detection accuracy and
improves the real-time capability. Our proposed scheme provides better performance for
the following reasons:
•Our proposed spatial and temporal variable selection rules are useful and consider both
raw traffic flow variables and some extended variables.
•The generated samples not only expand the sample size but also improve sample diver-
sity. Thus, generated samples can solve both the small sample size problem and the
imbalanced sample problem.
•The proposed TSSAE captures the correlations among the selected spatial and temporal
variables.
•The sample selection method selects samples just after incidents that improve the
detection model, giving it strong real-time characteristics.
Although the proposed hybrid model can effectively and efficiently detect traffic inci-
dents, some improvements could be made in future studies. First, external factors, such as
weather conditions, should be considered in the sample variables. Second, ranking the con-
tributions of the variables is important; future studies should improve the interpretability
of our model.
Acknowledgments
The authors thank the anonymous reviewers and authors of cited papers for their detailed comments,
without which this work would not have been possible.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
The project presented in this article is supported by the Sichuan New Generation Artificial Intelligence
Special Programme (No. 2018GZDZX0029) and the Shenzhen Science and Technology program (No.
KQTD20180412181337494).
References
Abdel-Aty, Mohamed, Nizam Uddin, Anurag Pande, Fathy Abdalla, and Liang Hsia. 2004. “Predict-
ing Freeway Crashes From Loop Detector Data by Matched Case-control Logistic Regression.”
Transportation Research Record: Journal of the Transportation Research Board 1897 (1): 88–95.
Adeli, H., and A. Samant. 2000. “An Adaptive Conjugate Gradient Neural Network–wavelet Model for
Traffic Incident Detection.” Computer-Aided Civil and Infrastructure Engineering 15 (4): 251–260.
Ahmed, Mohammed S., and Allen R. Cook. 1979. “Analysis of Freeway Traffic Time-Series Data by Using
Box-Jenkins Techniques.” Transportation Research Board Annual Meeting, 1979.
TRANSPORTMETRICA A: TRANSPORT SCIENCE 19
Asakura, Yasuo, Takahiko Kusakabe, Long Xuan Nguyen, and Takamasa Ushiki. 2017. “Incident Detec-
tion Methods Using Probe Vehicles with on-board GPS Equipment.” Transportation Research Part C:
Emerging Technologies 81: 330–341.
Bengio, Yoshua, Pascal Lamblin, Dan Popovici, and Hugo Larochelle. 2007. “Greedy Layer-Wise Train-
ing of Deep Networks.” In Advances in Neural Information Processing Systems, edited by Bernhard
Scholkopf, John C. Platt, and Thomas Hofmann, 153–160. Vancouver: MIT Press.
Chassiakos, Athanasios P., and Yorgos J. Stephanedes. 1993. “Smoothing Algorithms for Incident
Detection.” Transportation Research Record (1394): 8–16.
Chen, Shuyan, and Wei Wang. 2009. “Decision Tree Learning for Freeway Automatic Incident Detec-
tion.” Expert Systems with Applications 36 (2): 4101–4105.
Chen, Shuyan, Wei Wang, and Henk Van Zuylen. 2009. “Construct Support Vector Machine Ensemble
to Detect Traffic Incident.” Expert Systems with Applications 36 (8): 10976–10986.
Cookson, Graham. 2018. “Inrix Global Traffic Scorecard.”
Denton, Emily L., Soumith Chintala, Arthur Szlam, and Rob Fergus. 2015. “Deep Generative Image
Models using a Laplacian Pyramid of Adversarial Networks.” In Advances in Neural Information Pro-
cessing Systems, edited by Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and
Roman Garnett, 1486–1494. Montreal: MIT Press.
Ghosh, Arna, Biswarup Bhattacharya, and Somnath Basu Roy Chowdhury. 2016. “Sad-gan: Synthetic
Autonomous Driving using Generative Adversarial Networks.” arXiv preprint arXiv:1611.08788.
Ghosh, Bidisha, and Damien P. Smith. 2014. “Customization of Automatic Incident Detection
Algorithms for Signalized Urban Arterials.” Journal of Intelligent Transportation Systems 18 (4):
426–441.
Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron
Courville, and Yoshua Bengio. 2014. “Generative Adversarial Nets.” In Advances in Neural Infor-
mation Processing Systems, edited by Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D.
Lawrence, and Kilian Q. Weinberger, 2672–2680. Montreal: MIT Press.
Hall, Fred L., Yong Shi, and George Atala. 1993. “On-line Testing of the McMaster Incident Detection
Algorithm under Recurrent Congestion.”
Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. 2006. “A Fast Learning Algorithm for Deep
Belief Nets.” Neural Computation 18 (7): 1527–1554.
Hossain, Moinul, and Yasunori Muromachi. 2012. “A Bayesian Network Based Framework for Real-
time Crash Prediction on the Basic Freeway Segments of Urban Expressways.” Accident Analysis &
Prevention 45: 373–381.
Karim, Asim, and Hojjat Adeli. 2002. “Comparison of Fuzzy-wavelet Radial Basis Function Neural
Network Freeway Incident Detection Model with California Algorithm.” Journal of Transportation
Engineering 128 (1): 21–30.
Kuefler, Alex, Jeremy Morton, Tim Wheeler, and Mykel Kochenderfer. 2017. “Imitating Driver Behavior
with Generative Adversarial Networks.” In Intelligent Vehicles Symposium (IV), 2017 IEEE, 204–211.
IEEE.
Li, Linchao, Shanglu He, Jian Zhang, and Bin Ran. 2016a. “Short-term Highway Traffic Flow Predic-
tion Based on a Hybrid Strategy Considering Temporal–spatial Information.” Journal of Advanced
Transportation 50 (8): 2029–2040.
Li, Linchao, Shanglu He, Jian Zhang, and Fan Yang. 2016b. “Bagging-SVMs Algorithm-Based Traffic
Incident Detection.” CICTP 2016.
Li, Linchao, Lingqiao Qin, Xu Qu, Jian Zhang, Yonggang Wang, and Bin Ran. 2019. “Day-ahead Traffic
Flow Forecasting Based on a Deep Belief Network Optimized by the Multi-objective Particle Swarm
Algorithm.” Knowledge-Based Systems 172: 1–14.
Li, Linchao, Xi Sheng, Bowen Du, Yonggang Wang, and Bin Ran. 2020. “A Deep Fusion Model Based on
Restricted Boltzmann Machines for Traffic Accident Duration Prediction.” Engineering Applications
of Artificial Intelligence 93: 103686.
Li, Linchao, Jian Zhang, Yonggang Wang, and Bin Ran. 2018. “Missing Value Imputation for Traffic-
related Time Series Data Based on a Multi-view Learning Method.” IEEE Transactions on Intelligent
Transportation Systems 20 (8): 2933–2943.
20 L. LI ET AL.
Liang, Yunyi, Zhiyong Cui, Yu Tian, Huimiao Chen, and Yinhai Wang. 2018. “A Deep Generative Adver-
sarial Architecture for Network-Wide Spatial-Temporal Traffic State Estimation.” arXiv preprint
arXiv:1801.03818.
Lin, Yi, Linchao Li, Hailong Jing, Bin Ran, and Dongye Sun. 2020. “Automated Traffic Incident Detection
with a Smaller Dataset Based on Generative Adversarial Networks.” Accident Analysis & Preven-
tion144: 105628.
Liu, Qingchao, Jian Lu, and Shuyan Chen. 2013. “Traffic Incident Detection using Random Forest.”
Technical Report.
Lv, Yisheng, Yanjie Duan, Wenwen Kang, Zhengxi Li, and Fei-Yue Wang. 2015. “Traffic Flow Prediction
with Big Data: A Deep Learning Approach.” IEEE Transactions on Intelligent Transportation Systems16
(2): 865–873.
Ma, Xiaolei, Zhuang Dai, Zhengbing He, Jihui Ma, Yong Wang, and Yunpeng Wang. 2017. “Learning
Traffic As Images: a Deep Convolutional Neural Network for Large-scale Transportation Network
Speed Prediction.” Sensors 17 (4): 818.
Ma, Xiaolei, Haiyang Yu, Yunpeng Wang, and Yinhai Wang. 2015. “Large-scale Transportation Network
Congestion Evolution Prediction Using Deep Learning Theory.” PloS One 10 (3): e0119044.
Mak, Chin Long, and Henry S. L. Fan. 2007. “Development of Dual-station Automated Express-
way Incident Detection Algorithms.” IEEE Transactions on Intelligent Transportation Systems 8 (3):
480–490.
Mirza, Mehdi, and Simon Osindero. 2014. “Conditional Generative Adversarial Nets.” arXiv preprint
arXiv:1411.1784.
Odena, Augustus, Christopher Olah, and Jonathon Shlens. 2016. “Conditional Image Synthesis with
Auxiliary Classifier Gans.” arXiv preprint arXiv:1610.09585.
Qu, Xu, Wei Wang, Wen-fu Wang, and Pan Liu. 2017. “Real-time Rear-end Crash Potential Prediction
on Freeways.” Journal of Central South University 24 (11): 2664–2673.
Radford, Alec, Luke Metz, and Soumith Chintala. 2015. “Unsupervised Representation Learning with
Deep Convolutional Generative Adversarial Networks.” arXiv preprint arXiv:1511.06434.
Reed, Scott, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee.
2016. “Generative Adversarial Text to Image Synthesis.” arXiv preprint arXiv:1605.05396.
Samant, A., and H. Adeli. 2000. “Feature Extraction for Traffic Incident Detection Using Wavelet Trans-
form and Linear Discriminant Analysis.” Computer-Aided Civil and Infrastructure Engineering 15 (4):
241–250.
Samant, A., and H. Adeli. 2001. “Enhancing Neural Network Traffic Incident-detection Algorithms
Using Wavelets.” Computer-Aided Civil and Infrastructure Engineering 16 (4): 239–245.
Sheela, K. Gnana, and Subramaniam N. Deepa. 2013. “Review on Methods to Fix Number of Hidden
Neurons in Neural Networks.” Mathematical Problems in Engineering 2013: 1–11.
Wang, Ren, Shimao Fan, and Daniel B. Work. 2016. “Efficient Multiple Model Particle Filtering for
Joint Traffic State Estimation and Incident Detection.” Transportation Research Part C: Emerging
Technologies 71: 521–537.
Wang, Ren, Daniel B. Work, and Richard Sowers. 2016. “Multiple Model Particle Filter for Traffic Esti-
mation and Incident Detection.” IEEE Transactions on Intelligent Transportation Systems 17 (12):
3461–3470.
Wu, Yuankai, Huachun Tan, Lingqiao Qin, Bin Ran, and Zhuxi Jiang. 2018. “A Hybrid Deep Learn-
ing Based Traffic Flow Prediction Method and Its Understanding.” Transportation Research Part C:
Emerging Technologies 90: 166–180.
Xiao, Jianli, and Yuncai Liu. 2012. “Traffic Incident Detection Using Multiple-kernel Support Vector
Machine.” Transportation Research Record: Journal of the Transportation Research Board 2324 (1):
44–52.
Xu, Chengcheng, Wei Wang, and Pan Liu. 2013. “A Genetic Programming Model for Real-time
Crash Prediction on Freeways.” IEEE Transactions on Intelligent Transportation Systems 14 (2):
574–586.
Xu, Chengcheng, Wei Wang, Pan Liu, and Zhibin Li. 2015. “Calibration of Crash Risk Models on Free-
ways with Limited Real-time Traffic Data Using Bayesian Meta-analysis and Bayesian Inference
Approach.” Accident Analysis & Prevention 85: 207–218.
TRANSPORTMETRICA A: TRANSPORT SCIENCE 21
Yu, Rongjie, Xuesong Wang, Kui Yang, and Mohamed Abdel-Aty. 2016. “Crash Risk Analysis for Shang-
hai Urban Expressways: A Bayesian Semi-parametric Modeling Approach.” Accident Analysis &
Prevention 95: 495–502.
Yuan, Fang, and Ruey Long Cheu. 2003. “Incident Detection Using Support Vector Machines.” Trans-
portation Research Part C: Emerging Technologies 11 (3–4): 309–328.
Zhu, Lin, Fangce Guo, Rajesh Krishnan, and John W. Polak. 2018. “The Use of Convolutional Neural
Networks for Traffic Incident Detection at a Network Level.” Technical Report.
