New technologies and traffic data sources provide great potential to extend advanced strategies in freeway safety research. The High Definition Monitoring System (HDMS) data contribute comprehensive and precise individual vehicle information. This paper proposes an innovative Variable Speed Limit (VSL) based approach to manage crash risks by intervening in traffic flow dynamics on freeways using HDMS data. We first conducted an empirical analysis on real-time crash risk estimation using a binary logistic regression model. Then, intensive microscopic simulations based on AIMSUN were carried out to explore the effects of various intervention strategies with respect to a 3-lane freeway stretch in China. Different speed limits with distinct compliance rates under specified traffic conditions have been simulated. By taking into account the trade-off between safety benefits and delay in travel time, the speed limit strategies were optimized under various traffic conditions and the model with gradient feedback produces more satisfactory performance in controlling real-time crash risks. Last, the results were integrated into lane management strategies. This research can provide new ideas and methods to reveal the freeway crash risk evolution and active traffic management.
There is a growing body of evidence confirming a positive relationship between the road safety benefits and vehicle speed enforcement, especially on freeways. In China, for example, Shanghai and Jiangsu with intensive freeway networks are actively employing intelligent technology systems for coordinating traffic flow and improving road safety. Previous studies have highlighted the higher vehicle speed on freeways associated with increased crash risk and injury severity [
In the last decades, VSL has been intensively investigated on two main directions: traffic enhancement and safety improvement [
This study aims to apply real-time crash prediction in traffic control management. Previous studies on crash precursors have employed different kinds of traffic data such as loop detectors [
However, with respect to data type and resolution, the detectors are often limited and traffic data from continuous detectors cannot be collected or the collected data do not meet the requirements of the models. For instance, in China, detectors are installed far apart on freeways and most segments have not been equipped with detectors or surveillance devices. Regarding to method, generalized linear models could provide direct evidence of the traffic parameters’ impacts on crash risk. When dealing with highly nonlinear relationships between traffic flow and crash, it requires more computational, flexible, and nonlinear models [
New technologies and traffic data sources provide great potential to extend advanced strategies in freeway safety research. For example, the High Definition Monitoring System (HDMS) data contribute comprehensive and precise individual vehicle information, including vehicle type, speed, lane number, and plate number, as well as high quality photos captured by advanced vehicle license plate recognition systems. In China, HDMS have been installed on major freeways for public security management. The major contributions of this paper consist of the following aspects (Figure
Architecture of this study.
The study area is G15 Freeway in Nantong, Jiangsu Province, China, with a total length of approximately 100 km, from Sutong Bridge to Fuan Toll (as shown in Figure
The study area and subsegment division.
Data are obtained from the Public Security Traffic Managing System of the Traffic Management Research Institute, Ministry of Public Security. The freeway is a 6-lane one (3 lanes in each direction). The primary dataset includes all crash data and HDMS data from January to October 2016. The extracted HDMS data cover the lane number, direction, vehicle type and speed, recorded time of vehicle passing, etc. The study area includes five pairs of HD cameras.
The raw crash dataset includes 5924 crashes. However, the majority of crashes are not recorded with detailed location or direction information. 88% of the crashes are involved with multivehicles. Among them, 96% of the crashes are recorded with causes of hitting the fixed objects such as the guardrails and the medians, or hitting the unfixed objects such as the crash barriers. In order to investigate the impact of traffic dynamics on crashes, the traffic status prior to crashes has been examined as Figure
The traffic status 5-10 minutes prior to crashes.
First, the “max” curve shows the similar trend with that of the traditional capacity/speed curve. However, as under most conditions, the traffic state is normal and it is difficult to obtain the saturated flow state with different speeds. Hence, the “max” curve mainly reflects the nonfree flow state, in which the volume is approaching the maximum capacity. The area beyond the “max” curve reflects the chaos flow state or congestion state. Second, comparing the state of single-vehicle crashes and the state of multivehicles we could find that the single-vehicle crashes are more likely to occur within the 85th curve; i.e., the crashes are likely to occur under free flow conditions. This is consistent with several existing studies [
Additionally, as single-vehicle crashes are usually caused by random effects, such as driving distraction and breaking down, only multivehicles crashes with detailed temporal and spatial information are utilized in this study to investigate the relationship within traffic dynamics and crash risk. The data 5-10 minutes prior to the crashes are utilized to represent the traffic status prior to crashes. The method is commonly used in existing studies [
A matched case-control method is utilized to extract the related samples for each sampled crash. A 4:1 control-case ratio is used, as recommended in several previous studies [
Some filtering rules are also applied to select the available samples. Due to occasional HDMS system failure, some samples would be matched with invalid HDMS data or missing HDMS data. Noise and outliers are removed from the final dataset. Finally the crash dataset contains 171 samples and the control dataset has 618 non-crash samples. The summary statistics of variables are listed in Table
Summary statistics of variables.
Variable | Mean | S.D. | First Quartile | Third Quartile |
---|---|---|---|---|
| 103.59 | 84.80 | 43.00 | 138.00 |
| 80.37 | 12.83 | 75.04 | 87.93 |
| 22.28 | 5.40 | 18.73 | 26.08 |
Logistic regression analysis is commonly used to quantify the crash risk in real-time crash analysis. The traffic condition can be divided into two parts, crash cases (
Crash risk evaluation model for the whole segment.
Variables | | | | | |
---|---|---|---|---|---|
| 0.012 | 0.001 | 101.806 | 1 | 0.000 |
| 0.120 | 0.020 | 37.882 | 1 | 0.000 |
| -5.487 | 0.527 | 108.473 | 1 | 0.000 |
| |||||
| 0.755 | | 0.288 |
In order to compare the predicting ability of the HDMS data with the ability of the other kinds of traffic data, such as loop detector data [
Comparison of the performance of the three models.
| | | |
---|---|---|---|
AVI | 3 detectors per 2.35 km | Bayesian | 0.759 |
Loop detector | 6 detectors | Genetic | 0.608 |
HDMS | Single detector | Logistic Regression | 0.755 |
The spatial issue should be addressed for the implementation of VSL. Hence, another two models have been formulated to investigate the spatial effect, a downstream model and an upstream model. As shown in Figure
Spatial classification for downstream and upstream models.
As before, binary logistic regression has been used to estimate the crash risk models. The results are shown in Table
Crash risk evaluation models for downstream and upstream of the HDMS.
Variables | | | | | |
---|---|---|---|---|---|
| 0.012 | 0.001 | 64.270 | 1 | 0.000 |
| 0.139 | 0.026 | 27.961 | 1 | 0.000 |
| -5.912 | 0.714 | 68.650 | 1 | 0.000 |
| |||||
Downstream | 0.759 | ||||
| |||||
Variables | | | | | |
| |||||
| 0.013 | 0.002 | 38.924 | 1 | 0.000 |
| 0.093 | 0.029 | 10.320 | 1 | 0.001 |
| -4.948 | 0.776 | 40.608 | 1 | 0.000 |
| |||||
Upstream | 0.755 |
Results indicate that the performance of the crash risk models considering the spatial effects is similar to the performance of the crash risk model for the whole segment. The reason for this is that the crash risk is stable on each segment and the traffic parameters of adjacent locations on the same segment are highly correlated, which has been shown by Fang et al. [
In order to verify the method based on dynamic VSL control of crash risk, a sub-segment of the G15 Freeway segment utilized in Section
Aimsun API (Application Programming Interface) can be a helpful platform to evaluate certain traffic management strategies. We can obtain the necessary real-time traffic data (flow, speed, occupancy, etc.) with required aggregation levels or detailed vehicle information. The project is built with Visual C++ 6.0 based on Visual Studio 2005. Using Aimsun API functions, the detectors, VMS, and traffic control plans are modeled and the attributes are defined in our in-depth simulations.
To simulate the real HDMS data, AIMSUN API function is used to gather the real-time vehicle information. The scenarios are tested on the G15 Freeway with a design speed of 120km/h. In order to code the freeway segment in the simulation, Baidu Map GIS data source is utilized to build the freeway network. The drivers are assumed to comply with the speed limit, with a certain compliance rate, when the VSL starts to function on the segment.
The step size is 1 second. The Aimsun software development kit has been utilized to develop a module to extract the parameters for the crash risk evaluation model during the simulation process. The values for
The real-time crash risk probability (
The speed distribution and compliance level are calibrated before the simulations. The original speed limit for the G15 Freeway is 120 km/h and the proportion of vehicles with speed above 120 km/h is set as the non-compliance level. Drivers tend to speeding on the freeway as the freeway is designed with better alignments especially long stretch of straight line. The traffic on the freeway mainly comprises private cars and trucks. All the speed data for May 2016 is used to calibrate the parameters. Figure
Simulation scenario settings in Aimsun.
Scenario Parameters | Settings |
---|---|
Road type | Freeway |
Lane width | 3.75 meter |
Maximum speed limit | 120 km/h |
Detection cycle | 1 second |
Car following model | Minimum Headway+ deceleration estimation |
Global arrivals | Exponential |
Speed distributions of vehicles (May 2016).
Traffic spatial distribution should also be addressed to validate the simulations.
In existing studies, aggregate statistics have been validated such as the GEH statistics by FHWA [
Comparison of the distributions of time headway.
A set of six speed limits has been tested to evaluate the VSL performance under different flow conditions, namely 90, 80, 70, 60, 50, and 40 km/h. The traffic demand ranges from 2,000 to 5,000 veh/h. The Aimsun simulation results depend on the random seeds, reflecting the impact of random factors, and simulations were replicated five times to account for the variability. Each replication has 20 minutes to warm up with the traffic demand 2,000 veh/h and 60 minutes more to simulate the whole process with a different flow.
As the traffic is dynamic, the real traffic flow varies over time, as does the crash risk. Thus the objective is to keep the crash risk within an acceptable limit. In this study, the commonly used 85th percentile index in traffic safety is selected as the crash risk threshold for each replication; i.e., the traffic is evaluated as safe below that threshold. Once the crash risk exceeds the limit, proper strategies should be implemented to minimize the risk. As inappropriate speed limits would decrease the capacity and increase traffic delay, a comprehensive analysis should be made to achieve an optimal cost benefit ratio. Figure
Average delay and crash risk versus speed limit under different flow conditions.
Crash risk versus speed limits and flow conditions
Average delay versus speed limits and flow conditions
Figure
Speed limit impacts under different flow conditions.
| | | | | |
---|---|---|---|---|---|
5000 | 60 | 0.23 | 153.24 | 29.81 | 258.98 |
4000 | 70 | 0.27 | 158.92 | 39.73 | 167.51 |
3000 | 80 | 0.16 | 44.64 | 25.47 | 35.89 |
2000 | 120 | 0.09 | 17.94 | 0 | 0 |
Comparison of crash risk versus speed limit for alternative compliance rates under different flow conditions.
The objective of the VSL control strategy is to manage the traffic within an acceptable crash risk level and feedback is needed to adapt the strategy to the real-time traffic condition. Two kinds of strategies have been implemented in the simulations. The first is implementing and withdrawing the optimal VSL gradually (Strategy A) and the other is implementing and withdrawing the optimal VSL rapidly (Strategy B). Strategy A can be described as in Figure
Strategy A: the VSL strategy with gradient feedback control.
The simulations results with different strategies are shown in Figure
Simulation results under three conditions.
Average delays are 56.31s, 24.87s, and 91.84s for Raw, Strategy A, and Strategy B, respectively. Thus Strategy A generates the shortest travel time and this strategy could control the traffic condition efficiently and steadily, whereas improper speed limit implementation may lead to unexpected traffic congestions.
Results of this study demonstrate that the proposed VSL method could improve traffic safety, but more developments are required to produce integrated control strategies that are efficient and also applicable in real-time to large-scale networks [
Impact of lane management on crash risk and average delay under different traffic conditions.
Crash risk versus flow conditions and speed limits
Average delay versus flow conditions and speed limits
Figure
As shown in Figure
The study proposes an innovative dynamic variable speed approach through intervening in traffic flow dynamics. A binary logistic regression model based on HDMS data is built to estimate crash risk. HDMS data provide detailed vehicle information instead of aggregated data from loop detectors or other detectors. They provide better evidence on the crash mechanism. Microsimulations have been conducted with the AIMSUN simulation software. AIMSUN API is utilized to extract the detailed real-time vehicle information to calculate the crash risk. Different speed limits with several compliance rates under certain traffic conditions have been simulated. Considering the trade-off between safety benefits and travel time delay, we aim to optimize speed limit strategies under various traffic conditions.
Two kinds of VSL strategies have been applied to control the real-time crash risk in the simulated conditions of real traffic accidents; the strategy of implementing and withdrawing the optimal VSL gradually (gradient control) could provide better control effects and keep the crash risk at a lower level. Furthermore, lane management control has also been assessed. Results indicate that such integrated control could significantly reduce the crash risk without increasing average traffic delay. The trends in optimal integrated traffic control to reduce real-time crash risk prove to be promising.
Several potential directions are open for future exploration. For example, further work is being conducted to study the performance of applying the strategies into various road types or road network. In future studies, as more and more surveillance devices and vehicle on-board devices are installed, real-time data such as weather condition as well as driving behavior could be obtained. Meanwhile, with the continuous spatial distribution of surveillance devices and detectors, the aggregated traffic control of multiple segments could be investigated to achieve balanced traffic conditions in the road network as more driver-friendly integrated control strategies are developed to fit the new era of ITS.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that they have no conflicts of interest.
The research is supported by the National Key Research and Development Program (2016YFC0802701), the National Natural Science Foundation (71301119, 71871161, and 51708421), Shanghai Pujiang Program, and the Fundamental Research Funds for the Central Universities. The authors are indebted to Professor Xiaobo Qu for his insightful suggestions to improve this manuscript.