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The time-to-collision (TTC) index and its extended variants have been widely utilized to assess rear-end collision risks, but the characteristics of the time-series data have not been fully explored, especially for the transition from safe to risky conditions. This study proposes a novel approach in rear-end collision risk analysis based on the concept of transition durations. The vehicle trajectory data were extracted and the TTC index was used to identify risky and safe conditions. Three important transition durations are defined and their rationalities for evaluating rear-end collision risks are examined by developing random-parameters accelerated failure time (AFT) survival models. Furthermore, a typical case from real trajectory data is taken to discuss the limitations of using TTC and its variants, and the advantage of the proposed transition durations. The results of random-parameters AFT models reveal contributing factors affecting the length of three durations and demonstrate the rationality of transition durations in rear-end collision risks analysis. It is indicated that the proposed method outperforms TTC and its variants in evaluating rear-end collision risks, because it could not only provide the information of time point but also the variation of time-series data.
Ye Li; Dan Wu; Qinghong Chen; Jaeyoung Lee; Kejun Long. Exploring transition durations of rear-end collisions based on vehicle trajectory data: A survival modeling approach. Accident Analysis & Prevention 2021, 159, 106271 .
AMA StyleYe Li, Dan Wu, Qinghong Chen, Jaeyoung Lee, Kejun Long. Exploring transition durations of rear-end collisions based on vehicle trajectory data: A survival modeling approach. Accident Analysis & Prevention. 2021; 159 ():106271.
Chicago/Turabian StyleYe Li; Dan Wu; Qinghong Chen; Jaeyoung Lee; Kejun Long. 2021. "Exploring transition durations of rear-end collisions based on vehicle trajectory data: A survival modeling approach." Accident Analysis & Prevention 159, no. : 106271.
In this paper, the calculation method of the link travel time is firstly analysed in the continuous traffic flow by using the detection data collected when vehicles pass through urban links, and a theoretical derivation formula for estimating link travel time is proposed by considering the typical vehicle travel time and the time headway deviation upstream and downstream of the links as the main parameters. A typical vehicle analysis method based on link travel time similarity is proposed, and the theoretical formula is optimized, respectively. Then, an estimation formula based on maximum travel time similarity and an estimation formula based on maximum travel time confidence interval similarity are proposed, respectively. Finally, when analysing the fitting conditions, the collected data from urban roads in Nanjing are used to verify the proposed travel time estimation method based on the radio frequency identification devices. The results show that time headway deviation converges to zero when the hourly vehicle volume is more than 20 veh/h in the certain flow direction, and there are more positive and negative fluctuations when the hourly vehicle volume is less than 10 veh/h in the certain flow direction. The accuracy of the proposed improved method based on typical vehicle travel time estimation is significantly improved by considering the typical vehicle travel time, and typical vehicles on the road segment mainly exist at the tail of the traffic platoon in the corresponding period.
Jian Gu; Miaohua Li; Linghua Yu; Shun Li; Kejun Long. Analysis on Link Travel Time Estimation considering Time Headway Based on Urban Road RFID Data. Journal of Advanced Transportation 2021, 2021, 1 -19.
AMA StyleJian Gu, Miaohua Li, Linghua Yu, Shun Li, Kejun Long. Analysis on Link Travel Time Estimation considering Time Headway Based on Urban Road RFID Data. Journal of Advanced Transportation. 2021; 2021 ():1-19.
Chicago/Turabian StyleJian Gu; Miaohua Li; Linghua Yu; Shun Li; Kejun Long. 2021. "Analysis on Link Travel Time Estimation considering Time Headway Based on Urban Road RFID Data." Journal of Advanced Transportation 2021, no. : 1-19.
Lane-changing is a complicated task and has a high probability of accident occurrence. Although a large body of literature has used vehicle trajectories to microscopically understand and model lane-changing behavior, most of these studies focus on lane-changing decision making and lane changing's impacts on surrounding vehicles, not on traffic safety. The contributing factors to lane-changing risks have not been fully explored from the perspective of microscopic behavior using vehicle trajectory data. This study investigates the contributing factors to accident risks in different lane-changing patterns with taking unobserved heterogeneity into account. A vehicle trajectory dataset, HighD is used and 4842 lane-changing vehicle groups are extracted for analysis. These vehicle groups are divided into sixteen patterns according to the vehicle type, and three major patterns are examined. A lane-changing risk index (LCRI) is proposed to evaluate the risk level of each vehicle group. Two methods are developed and compared for exploring lane-changing risks of the three patterns including (1) establishing the random parameters fractional logit models; and (2) classifying LCRI by k-means algorithm and establishing random parameters ordered logit models with heterogeneity in means and variances. The modeling results show that the latter method performs better and the risk level of the vehicle group is strongly associated with (1) the mean and standard deviation of the gap distance between vehicles; (2) the longitudinal velocities and acceleration of vehicles; and (3) the lane-changing direction and duration. However, different patterns are found to have different contributing variables and effects. The effects of gap distances vary considerably across different vehicle groups and the longitudinal velocity of vehicles are associated with the means of random parameters for gap distance.
Qinghong Chen; Helai Huang; Ye Li; Jaeyoung Lee; Kejun Long; Ruifeng Gu; Xiaoqi Zhai. Modeling accident risks in different lane-changing behavioral patterns. Analytic Methods in Accident Research 2021, 30, 100159 .
AMA StyleQinghong Chen, Helai Huang, Ye Li, Jaeyoung Lee, Kejun Long, Ruifeng Gu, Xiaoqi Zhai. Modeling accident risks in different lane-changing behavioral patterns. Analytic Methods in Accident Research. 2021; 30 ():100159.
Chicago/Turabian StyleQinghong Chen; Helai Huang; Ye Li; Jaeyoung Lee; Kejun Long; Ruifeng Gu; Xiaoqi Zhai. 2021. "Modeling accident risks in different lane-changing behavioral patterns." Analytic Methods in Accident Research 30, no. : 100159.
This paper comprehensively discusses the cooperative communication and computation of vehicular system. Based on the cooperative transmission, an stochastic model of vehicle-to-vehicle (V2V) communication reliability is established using probability theory. Furthermore, the computation reliability is defined as a new metric for computation offloading, and a vehicle computational performance evaluation model is also established. In order to effectively compute the required data, we combine V2V communication and vehicle computing to further characterize the coupling reliability of cooperative communications and computation systems. In addition, we propose a virtual queue model that combines queue length and vehicle privacy entropy to optimize partitioning. Finally, considering the amount of processing data and cut-off time of vehicle applications, we establish the optimal partition model of vehicle computing with the goal of maximizing the coupling reliability, and propose the coupling-oriented reliability calculation for vehicle collaboration using dynamic programming methods. Simulations show that the proposed scheme outperforms traditional approaches in terms of coupling reliability and completion rate. In addition, the allocation between local computing and data offloading is controlled by the server's privacy perception of collaboration events.
Xu Han; Daxin Tian; Zhengguo Sheng; Xuting Duan; Jianshan Zhou; Wei Hao; Kejun Long; Min Chen; Victor C. M. Leung. Reliability-Aware Joint Optimization for Cooperative Vehicular Communication and Computing. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 5437 -5446.
AMA StyleXu Han, Daxin Tian, Zhengguo Sheng, Xuting Duan, Jianshan Zhou, Wei Hao, Kejun Long, Min Chen, Victor C. M. Leung. Reliability-Aware Joint Optimization for Cooperative Vehicular Communication and Computing. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (8):5437-5446.
Chicago/Turabian StyleXu Han; Daxin Tian; Zhengguo Sheng; Xuting Duan; Jianshan Zhou; Wei Hao; Kejun Long; Min Chen; Victor C. M. Leung. 2020. "Reliability-Aware Joint Optimization for Cooperative Vehicular Communication and Computing." IEEE Transactions on Intelligent Transportation Systems 22, no. 8: 5437-5446.
This paper advances the issue of Transit Signal Priority (TSP) control by introducing an application to multi-route bus conflicting requests, capitalizing on the headways and improved total delay of a multi-route bus network. The headway-based TSP accommodating conflicting requests overcomes the shortcoming by the traditional “First Arrival, First Serve” strategy and presents significant improvement on bus service performance. According to the bus arrival time, expected headway, and headway deviation value, we establish an optimal signal control model which aiming to minimize the deviation between the bus headway and the expected headway. The case study analysis conducts three schemes: background cycle-based TSP, total delay-based TSP, and headway deviation-based TSP. The performance of headway-based TSP is compared against other two schemes under three different intersection scenarios. The results show that the headway-based TSP has the best effect on improving and balancing the headway stability and distribution. Compared with the scheme of background cycle and the minimum total bus delay, the bus headway deviation is decreased by 42.05% and 28.64% respectively. Compared with the scheme of background cycle, the bus parking delay time is decreased by 36%.
Kejun Long; Junjun Wei; Jian Gu; Xiaoguang Yang. Headway-Based Multi-Route Transit Signal Priority at Isolated Intersection. IEEE Access 2020, 8, 187824 -187831.
AMA StyleKejun Long, Junjun Wei, Jian Gu, Xiaoguang Yang. Headway-Based Multi-Route Transit Signal Priority at Isolated Intersection. IEEE Access. 2020; 8 (99):187824-187831.
Chicago/Turabian StyleKejun Long; Junjun Wei; Jian Gu; Xiaoguang Yang. 2020. "Headway-Based Multi-Route Transit Signal Priority at Isolated Intersection." IEEE Access 8, no. 99: 187824-187831.
Metros are usually built and added on the basis of a completed bus network in Chinese cities. After the metro construction, it is faced with the problem of how to adjust and optimize the original bus lines based on the new metro system. This research mainly proposes a bus line optimization method based on bus and metro integration. In the consideration of the geographical space, the cooperation and competition relationship between bus and metro lines is qualitatively introduced according to the geographical location and service range of metro (800 m radius) and bus (500 m radius) stations. The competition and cooperation indexes are applied to define the co-opetition relationship between bus and metro lines. The bus line optimization model is constructed based on the co-opetition coefficient and Changsha Metro Line Number 2 is chosen as a case study to verify the optimization model. The results show that the positive competition, efficient cooperation, and travel efficiency between metro and bus has been significantly enhanced after optimization. Moreover, this paper provides a reasonable reference for public transport network planning and resource allocation.
Junjun Wei; Kejun Long; Jian Gu; Qingling Ju; Piao Zhu. Optimizing Bus Line Based on Metro-Bus Integration. Sustainability 2020, 12, 1493 .
AMA StyleJunjun Wei, Kejun Long, Jian Gu, Qingling Ju, Piao Zhu. Optimizing Bus Line Based on Metro-Bus Integration. Sustainability. 2020; 12 (4):1493.
Chicago/Turabian StyleJunjun Wei; Kejun Long; Jian Gu; Qingling Ju; Piao Zhu. 2020. "Optimizing Bus Line Based on Metro-Bus Integration." Sustainability 12, no. 4: 1493.
Freeway travel time is influenced by many factors including traffic volume, adverse weather, accidents, traffic control, and so on. We employ the multiple source data-mining method to analyze freeway travel time. We collected toll data, weather data, traffic accident disposal logs, and other historical data from Freeway G5513 in Hunan Province, China. Using the Support Vector Machine (SVM), we proposed the travel time predicting model founded on these databases. The new SVM model can simulate the nonlinear relationship between travel time and those factors. In order to improve the precision of the SVM model, we applied the Artificial Fish Swarm algorithm to optimize the SVM model parameters, which include the kernel parameter σ, non-sensitive loss function parameter ε, and penalty parameter C. We compared the new optimized SVM model with the Back Propagation (BP) neural network and a common SVM model, using the historical data collected from freeway G5513. The results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM model, respectively.
Kejun Long; Wukai Yao; Jian Gu; Wei Wu; Lee D. Han. Predicting Freeway Travel Time Using Multiple- Source Heterogeneous Data Integration. Applied Sciences 2018, 9, 104 .
AMA StyleKejun Long, Wukai Yao, Jian Gu, Wei Wu, Lee D. Han. Predicting Freeway Travel Time Using Multiple- Source Heterogeneous Data Integration. Applied Sciences. 2018; 9 (1):104.
Chicago/Turabian StyleKejun Long; Wukai Yao; Jian Gu; Wei Wu; Lee D. Han. 2018. "Predicting Freeway Travel Time Using Multiple- Source Heterogeneous Data Integration." Applied Sciences 9, no. 1: 104.
The mechanisms of traffic congestion generation are more than complicated, due to complex geometric road designs and complicated driving behavior at urban expressways in China. We employ a cell transmission model (CTM) to simulate the traffic flow spatiotemporal evolution process along the expressway, and reveal the characteristics of traffic congestion occurrence and propagation. Here, we apply the variable-length-cell CTM to adapt the complicated road geometry and configuration, and propose the merge section CTM considering drivers’ mandatory lane-changing and other unreasonable behavior at the on-ramp merge section, and propose the diverge section CTM considering queue length end extending the expressway mainline to generate a dynamic bottleneck at the diverge section. In the new improved CTM model, we introduce merge ratio and diverge ratio to describe the effect of driver behavior at the merge and diverge section. We conduct simulations on the real urban expressway in China, with results showing that the merge section and diverge section are the original location of expressway traffic congestion generation, and on/off-ramp traffic flow has a great effect on the expressway mainline operation. When on-ramp traffic volume increases by 40%, the merge section delay increases by 35%, and when off-ramp capacity increases by 100 veh/hr, the diverge section delay decreases about by 10%, which proves the strong interaction between expressway and adjacent road networks. Our results provide the underlying insights of traffic congestion mechanism in urban expressway in China, which can be used to better understand and manage this issue.
Kejun Long; Qin Lin; Jian Gu; Wei Wu; Lee D. Han. Exploring Traffic Congestion on Urban Expressways Considering Drivers’ Unreasonable Behavior at Merge/Diverge Sections in China. Sustainability 2018, 10, 4359 .
AMA StyleKejun Long, Qin Lin, Jian Gu, Wei Wu, Lee D. Han. Exploring Traffic Congestion on Urban Expressways Considering Drivers’ Unreasonable Behavior at Merge/Diverge Sections in China. Sustainability. 2018; 10 (12):4359.
Chicago/Turabian StyleKejun Long; Qin Lin; Jian Gu; Wei Wu; Lee D. Han. 2018. "Exploring Traffic Congestion on Urban Expressways Considering Drivers’ Unreasonable Behavior at Merge/Diverge Sections in China." Sustainability 10, no. 12: 4359.
Freeway travelling time is affected by many factors including traffic volume, adverse weather, accident, traffic control and so on. We employ the multiple source data-mining method to analyze freeway travelling time. We collected toll data, weather data, traffic accident disposal logs and other historical data of freeway G5513 in Hunan province, China. Using Support Vector Machine (SVM), we proposed the travelling time model based on these databases. The new SVM model can simulate the nonlinear relationship between travelling time and those factors. In order to improve the precision of the SVM model, we applied Artificial Fish Swarm algorithm to optimize the SVM model parameters, which include the kernel parameter σ, non-sensitive loss function parameter ε, and penalty parameter C. We compared the new optimized SVM model with Back Propagation (BP) neural network and common SVM model, using the historical data collected from freeway G5513. The results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM model respectively.
Kejun Long; Wukai Yao; Jian Gu; Wei Wu. Predicting Freeway Travelling Time Using Multiple-Source Data. 2018, 1 .
AMA StyleKejun Long, Wukai Yao, Jian Gu, Wei Wu. Predicting Freeway Travelling Time Using Multiple-Source Data. . 2018; ():1.
Chicago/Turabian StyleKejun Long; Wukai Yao; Jian Gu; Wei Wu. 2018. "Predicting Freeway Travelling Time Using Multiple-Source Data." , no. : 1.
Minghua Zeng; Kejun Long; Lee D. Han; Xiaoguang Yang. Optimization of Degradable Road Network Considering VMS Information and Heterogeneous ATIS Users. Journal of Computing in Civil Engineering 2017, 31, 04017048 .
AMA StyleMinghua Zeng, Kejun Long, Lee D. Han, Xiaoguang Yang. Optimization of Degradable Road Network Considering VMS Information and Heterogeneous ATIS Users. Journal of Computing in Civil Engineering. 2017; 31 (5):04017048.
Chicago/Turabian StyleMinghua Zeng; Kejun Long; Lee D. Han; Xiaoguang Yang. 2017. "Optimization of Degradable Road Network Considering VMS Information and Heterogeneous ATIS Users." Journal of Computing in Civil Engineering 31, no. 5: 04017048.
Most previous works associated with transit signal priority merely focus on the optimization of signal timings, ignoring both bus speed and dwell time at bus stops. This paper presents a novel approach to optimize the holding time at bus stops, signal timings, and bus speed to provide priority to buses at isolated intersections. The objective of the proposed model is to minimize the weighted average vehicle delays of the intersection, which includes both bus delay and impact on nearby intersection traffic, ensuring that buses clear these intersections without being stopped by a red light. A set of formulations are developed to explicitly capture the interaction between bus speed, bus holding time, and transit priority signal timings. Experimental analysis is used to show that the proposed model has minimal negative impacts on general traffic and outperforms the no priority, signal priority only, and signal priority with holding control strategies (no bus speed adjustment) in terms of reducing average bus delays and stops. A sensitivity analysis further demonstrates the potential of the proposed approach to be applied to bus priority control systems in real-time under different traffic demands, bus stop locations, and maximum speed limits. Copyright © 2016 John Wiley & Sons, Ltd.
Wei Wu; Wanjing Ma; Kejun Long; Yinhai Wang. Integrated optimization of bus priority operations in connected vehicle environment. Journal of Advanced Transportation 2016, 50, 1853 -1869.
AMA StyleWei Wu, Wanjing Ma, Kejun Long, Yinhai Wang. Integrated optimization of bus priority operations in connected vehicle environment. Journal of Advanced Transportation. 2016; 50 (8):1853-1869.
Chicago/Turabian StyleWei Wu; Wanjing Ma; Kejun Long; Yinhai Wang. 2016. "Integrated optimization of bus priority operations in connected vehicle environment." Journal of Advanced Transportation 50, no. 8: 1853-1869.
Developing public transportation and giving priority to buses is a feasible solution for improving the level of public transportation service, which facilitates congestion alleviation and prevention, and contributes to urban development and city sustainability. This paper presents a novel bus operation control strategy including both holding control and speed control to improve the level of service of transit systems within a connected vehicle environment. Most previous work focuses on optimization of signal timing to decrease the bus signal delay by assuming that holding control is not applied; the speed of buses is given as a constant input and the acceleration and deceleration processes of buses can be neglected. This paper explores the benefits of a bus operation control strategy to minimize the total cost, which includes bus signal delay, bus holding delay, bus travel delay, acceleration cost due to frequent stops and intense driving. A set of formulations are developed to explicitly capture the interaction between bus holding control and speed control. Experimental analysisand simulation tests have shown that the proposed integrated operational model outperforms the traditional control, speed control only, or holding control only strategies in terms of reducing the total cost of buses. The sensitivity analysis has further demonstrated the potential effectiveness of the proposed approach to be applied in a real-time bus operation control system under different levels of traffic demand, bus stop locations, and speed limits.
Wei Wu; Wanjing Ma; Kejun Long; Heping Zhou; Yi Zhang. Designing Sustainable Public Transportation: Integrated Optimization of Bus Speed and Holding Time in a Connected Vehicle Environment. Sustainability 2016, 8, 1170 .
AMA StyleWei Wu, Wanjing Ma, Kejun Long, Heping Zhou, Yi Zhang. Designing Sustainable Public Transportation: Integrated Optimization of Bus Speed and Holding Time in a Connected Vehicle Environment. Sustainability. 2016; 8 (11):1170.
Chicago/Turabian StyleWei Wu; Wanjing Ma; Kejun Long; Heping Zhou; Yi Zhang. 2016. "Designing Sustainable Public Transportation: Integrated Optimization of Bus Speed and Holding Time in a Connected Vehicle Environment." Sustainability 8, no. 11: 1170.
A protected left turn phase is often used at intersections with heavy left turns. This may induce a capacity gap between adjacent intersections along the arterial road among which only parts of intersection are with protected left turn phase. A model for integrated optimization of protected left turn phases for adjacent intersections along the arterial road is developed to solve this problem. Two objectives are considered: capacity gap minimization and capacity maximization. The problems are formulated as Binary-Integer-Linear-Programs, which are solvable by standard branch-and-bound routine. A set of constraints have been set up to ensure the feasibility of the resulting optimal left turn phase type and signal settings. A field intersections group of the Wei-er Road of Ji’nan city is used to test the proposed model. The results show that the method can decrease the capacity gap between adjacent intersections, reduce the delay as well as increase the capacity in comparison with the field signal plan and signal plan optimized by Synchro. The sensitivity analysis has further demonstrated the potential of the proposed approach to be applied in coordinated design of left turn phases between adjacent intersections along the arterial road under different traffic demandpatterns.
Wei Wu; Wanjing Ma; Kejun Long. Capacity Matching Based Model for Protected Left Turn Phases Design of Adjacent Signalized Intersections Along Arterial Roads. Promet - Traffic&Transportation 2015, 27, 13 -21.
AMA StyleWei Wu, Wanjing Ma, Kejun Long. Capacity Matching Based Model for Protected Left Turn Phases Design of Adjacent Signalized Intersections Along Arterial Roads. Promet - Traffic&Transportation. 2015; 27 (1):13-21.
Chicago/Turabian StyleWei Wu; Wanjing Ma; Kejun Long. 2015. "Capacity Matching Based Model for Protected Left Turn Phases Design of Adjacent Signalized Intersections Along Arterial Roads." Promet - Traffic&Transportation 27, no. 1: 13-21.
Simulation-based study is one of the major methods for evacuation planning. How to quickly build an origin-destination (OD) matrix from each source zone to their nearest destination becomes an issue. In this paper, we propose a new problem - Multiple-Source, Nearest Destination, Shortest Path (MSNDSP) - for generating an OD matrix in evacuation assignments. Compared to a benchmark study using Dijkstra's algorithm, we propose a new Super Node-based Trip Generator (SNTG) algorithm to improve the computing performance. The new algorithm significantly reduces the computational time through transforming the MSNDSP problem to a normal single-source, shortest path problem with a super-node concept. Experimental studies using real-world street networks and high-resolution LandScan USA population data indicate that the SNTG algorithm can provide OD output identical to the benchmark study, but the computing time is about 500 to 45,000 times faster in different network sizes. Discussion of this algorithm in other applications is also conducted.
Wei Lu; Lee D. Han; Cheng Liu; Kejun Long. A Multiple-Source, Nearest Destination, Shortest Path Problem in Evacuation Assignments. CICTP 2014 2014, 3691 -3702.
AMA StyleWei Lu, Lee D. Han, Cheng Liu, Kejun Long. A Multiple-Source, Nearest Destination, Shortest Path Problem in Evacuation Assignments. CICTP 2014. 2014; ():3691-3702.
Chicago/Turabian StyleWei Lu; Lee D. Han; Cheng Liu; Kejun Long. 2014. "A Multiple-Source, Nearest Destination, Shortest Path Problem in Evacuation Assignments." CICTP 2014 , no. : 3691-3702.
Kejun Long; Yue Liu; Lee D. Han. Impact of countdown timer on driving maneuvers after the yellow onset at signalized intersections: An empirical study in Changsha, China. Safety Science 2013, 54, 8 -16.
AMA StyleKejun Long, Yue Liu, Lee D. Han. Impact of countdown timer on driving maneuvers after the yellow onset at signalized intersections: An empirical study in Changsha, China. Safety Science. 2013; 54 ():8-16.
Chicago/Turabian StyleKejun Long; Yue Liu; Lee D. Han. 2013. "Impact of countdown timer on driving maneuvers after the yellow onset at signalized intersections: An empirical study in Changsha, China." Safety Science 54, no. : 8-16.
Objectives: Few studies have focused on the effect of countdown timers at signalized intersections in China, where such timers are widely deployed for their perceived benefits of increased safety and capacity. This study examines the effect of countdown timers on driver behavior during the yellow interval. Method: Signal phasing and traffic operations were videotaped at 4 comparable signalized intersections under normal conditions. Microscopic details were extracted manually at 25 Hz to yield 24 h of data on onset time of the yellow, onset time of the red, driver location and actions after the onset of the yellow, red light–running violations, etc. For comparable intersections with and without countdown timers, driver behavior measured by driver decision (stop or go) and vehicle entry time (when the vehicle crosses the stop line) were analyzed using binary logistical regression (BLR) and a nonparametric test, respectively. Results: The results suggest that countdown timers can indeed influence driver behaviors, in terms of decisions to stop or cross the intersection as well as the distribution of vehicle entry times. There was a strong correlation between the presence of countdown timers and an increase in red light violations. Conclusion: Countdown timers may lead to increased entrance into the intersection during the later portions of the yellow and even the red. This alarming finding calls for further research as well as for serious consideration before the field deployment of countdown timers.
Kejun Long; Lee D. Han; Qiang Yang. Effects of Countdown Timers on Driver Behavior After the Yellow Onset at Chinese Intersections. Traffic Injury Prevention 2011, 12, 538 -544.
AMA StyleKejun Long, Lee D. Han, Qiang Yang. Effects of Countdown Timers on Driver Behavior After the Yellow Onset at Chinese Intersections. Traffic Injury Prevention. 2011; 12 (5):538-544.
Chicago/Turabian StyleKejun Long; Lee D. Han; Qiang Yang. 2011. "Effects of Countdown Timers on Driver Behavior After the Yellow Onset at Chinese Intersections." Traffic Injury Prevention 12, no. 5: 538-544.
Ke-Jun Long; Lee D Han; Sai-Zheng Wang. Shortest path search in road network with incomplete information. Journal of Computer Applications 2011, 31, 651 -653.
AMA StyleKe-Jun Long, Lee D Han, Sai-Zheng Wang. Shortest path search in road network with incomplete information. Journal of Computer Applications. 2011; 31 (3):651-653.
Chicago/Turabian StyleKe-Jun Long; Lee D Han; Sai-Zheng Wang. 2011. "Shortest path search in road network with incomplete information." Journal of Computer Applications 31, no. 3: 651-653.