scholarly journals Risk Analysis of Vehicle Rear-End Collisions at Intersections

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Sheng Dong ◽  
Minjie Zhang ◽  
Zhenjiang Li
Keyword(s):  
Decision Making ◽  
Travel Time ◽  
Evaluation Method ◽  
Red Light ◽  
Past Research ◽  
Traffic Flows ◽  
Trajectory Data ◽  
Driving Behaviors ◽  
Stop And Go ◽  
Binary Logit

Aiming at solving a typical problem of past research using accident experience statistics of being unable to adapt to changing traffic flows, this paper provides an evaluation method of the risk of vehicle rear-end collisions at red-light-camera (RLC) intersections based on theoretical probabilities. Taking advantage of trajectory data of vehicles at the two similar intersections, which are Cao’an Road and Lvyuan Road with RLCs and Cao’an Road and Anhong Road without RLCs in Shanghai, a binary logit (BL) model of stop-and-go decision-making is established. Using the model and adjusting the headway and potential travel time, we can perform simulation and analysis of rear-end collisions. The result shows that this method is feasible to analyse the influence of RLCs on rear-end collisions. The analysis indicates that RLCs can cause higher speeds for vehicles passing the RLC intersection and more abnormal driving behaviors, which increase the difficulty of stop-and-go decision-making. RLCs do not always lead to an increase of rear-end collisions. For vehicles close to or far from intersection at the decision-making time, RLCs will significantly reduce the possibility of rear-end collisions; however, for vehicles in the potential travel time of 2 s∼3 s, RLCs will increase the probability of rear-end collisions.

Comparative study on e-bikes’ decision-making behaviors under flashing green and green countdown

2021 ◽  
pp. 1-8
Author(s):  
Haoyang Meng ◽  
Sheng Dong ◽  
Jibiao Zhou ◽  
Shuichao Zhang ◽  
Zhenjiang Li
Keyword(s):  
Decision Making ◽  
Critical Issue ◽  
Trajectory Data ◽  
Decision Points ◽  
Binary Logit ◽  
The One ◽  
Common Signal ◽  
And Function ◽  
Decision Behaviors ◽  
Stop Line

Green flash light (FG) and green countdown (GC) are the two most common signal formats applied in green-red transition that provides drivers additional alert before termination of green phase. Due to their importance and function in stop-pass decision-making process, proper use of them has become a critical issue to greatly improve the safety and efficiency of signalized intersections. Gradually e-bike riders have become more important commuters in China, however, the influence of FG or GC on them is not clear yet and need pay more attention to it. This study chooses two almost identical intersections to obtain highly accurate trajectory data of e-bike riders to study their decision-making behaviors under FG or GC. The e-bike riders’ behavior is classified into four categories and is to identify their stop-pass decision points using the acceleration trend. Two binary-logit models were built to predict the stop–pass decision behaviors for the different e-bike rider groups, explaining that the potential time to the stop-line is the dominant independent factor of the different behaviors of GC and FG. Furthermore empirical analysis of decision points indicated that GC provides the earlier stop-pass decision point and longer decision making duration on the one side while results in more complexity of decision making and greater risk of stop-line crossing than FG on the other side.

scholarly journals Exploring the Influence of E-Hailing Applications on the Taxi Industry—From the Perspective of the Drivers

2021 ◽  
Vol 10 (2) ◽  
pp. 77
Author(s):  
Yitong Gan ◽  
Hongchao Fan ◽  
Wei Jiao ◽  
Mengqi Sun
Keyword(s):  
Driving Behavior ◽  
Urban Traffic ◽  
Behavior Changes ◽  
Transportation Industry ◽  
Trajectory Data ◽  
Long Distance ◽  
Taxi Drivers ◽  
Driving Behaviors ◽  
Taxi Industry ◽  
The Times

In China, the traditional taxi industry is conforming to the trend of the times, with taxi drivers working with e-hailing applications. This reform is of great significance, not only for the taxi industry, but also for the transportation industry, cities, and society as a whole. Our goal was to analyze the changes in driving behavior since taxi drivers joined e-hailing platforms. Therefore, this paper mined taxi trajectory data from Shanghai and compared the data of May 2015 with those of May 2017 to represent the before-app stage and the full-use stage, respectively. By extracting two-trip events (i.e., vacant trip and occupied trip) and two-spot events (i.e., pick-up spot and drop-off spot), taxi driving behavior changes were analyzed temporally, spatially, and efficiently. The results reveal that e-hailing applications mine more long-distance rides and new pick-up locations for drivers. Moreover, driver initiative have increased at night since using e-hailing applications. Furthermore, mobile payment facilities save time that would otherwise be taken sorting out change. Although e-hailing apps can help citizens get taxis faster, from the driver’s perspective, the apps do not reduce their cruising time. In general, e-hailing software reduces the unoccupied ratio of taxis and improves the operating ratio. Ultimately, new driving behaviors can increase the driver’s revenue. This work is meaningful for the formulation of reasonable traffic laws and for urban traffic decision-making.

Estimation of Path Travel Time Distributions in Stochastic Time-Varying Networks with Correlations

2021 ◽  
pp. 036119812110184
Author(s):  
Monika Filipovska ◽  
Hani S. Mahmassani ◽  
Archak Mittal
Keyword(s):  
Time Dependence ◽  
Travel Time ◽  
Correlation Structure ◽  
Time Varying ◽  
Travel Times ◽  
Trajectory Data ◽  
Time Varying Networks ◽  
Transportation Research ◽  
Search Approach ◽  
Stochastic Time

Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective, the performance of the network is experienced at the level of a path, and, as such, knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation (MCS)-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the MCS approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches, and that its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a MCS approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

Study of highway asphalt pavement recycling maintenance scheme decision system and decision method

2021 ◽  
pp. 1-10
Author(s):  
Hongjun Jing ◽  
Ping Yao ◽  
Lichen Song ◽  
Jiahao Zhang ◽  
Yanlong Zhao ◽  
...  
Keyword(s):  
Decision Making ◽  
Evaluation System ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Asphalt Pavement ◽  
Economic Benefits ◽  
Highway Maintenance ◽  
Decision System ◽  
Recovery Index ◽  
Pavement Recycling

With modern economic and social development, the technical and economic requirements of highway maintenance and construction projects have become increasingly complicated. Meticulous and in-depth investigation and demonstration guided by scientific theories and methods are of considerable importance to highway maintenance scheme decision. As basis for the selection of highway asphalt pavement recycling maintenance scheme, the factors influencing the decision are analytically demonstrated and an evaluation system is proposed, including three major decision indexes: applicability of recycling mode, recycled pavement quality recovery index, and economic benefit. According to the principles of data statistics and analysis, this study proposes a calculation method for the recycled pavement quality recovery index, analyzes the economic benefits of decision schemes using economic models such as recycling ratio and cost, and puts forward an optimal evaluation method of engineering cost and its fuzzy score intervals. Index weights are calculated through the analytic hierarchy process, and the comprehensive decision evaluation system and comprehensive evaluation method are established. Subsequently, the decision-making method is analyzed on the basis of the decision system by combining the related data. Results show maximum weight of the pavement quality recovery index and minor differences among four recycling schemes in the quality recovery index and applicability. The decision-making results are simplified with clear hierarchical feature because of the fuzzy score intervals of each index. Findings can provide a reference for the asphalt pavement recycling scheme decision.

Real-Time Prediction of Vehicle Trajectories for Proactively Identifying Risky Driving Behaviors at High-Speed Intersections

2018 ◽  
Vol 2672 (38) ◽  
pp. 233-244 ◽  
Author(s):  
Chaopeng Tan ◽  
Nan Zhou ◽  
Fen Wang ◽  
Keshuang Tang ◽  
Yangbeibei Ji
Keyword(s):  
Phase Transition ◽  
High Speed ◽  
Red Light ◽  
Risky Behaviors ◽  
Risky Driving ◽  
Red Light Running ◽  
Driving Behaviors ◽  
Vehicle Trajectories ◽  
Single Vehicle ◽  
Risky Driving Behaviors

At high-speed intersections in many Chinese cities, a traffic-light warning sequence at the end of the green phase—three seconds of flashing green followed by three seconds of yellow—is commonly implemented. Such a long phase transition time leads to heterogeneous decision-making by approaching drivers as to whether to pass the signal or stop. Therefore, risky driving behaviors such as red-light running, abrupt stop, and aggressive pass are more likely to occur at these intersections. Proactive identification of risky behaviors can facilitate mitigation of the dilemma zone and development of on-board safety altering strategies. In this study, a real-time vehicle trajectory prediction method is proposed to help identify risky behaviors during the signal phase transition. Two cases are considered and treated differently in the proposed method: a single vehicle case and a following vehicle case. The adaptive Kalman filter (KF) model and the K-nearest neighbor model are integrated to predict vehicle trajectories. The adaptive KF model and intelligent driver model are fused to predict the following vehicles’ trajectories. The proposed models are calibrated and validated using 1,281 vehicle trajectories collected at three high-speed intersections in Shanghai. Results indicate that the root mean square error between the predicted trajectories and the actual trajectories is 5.02 m for single vehicles and 2.33 m for following vehicles. The proposed method is further applied to predict risky behaviors, including red-light running, abrupt stop, aggressive pass, speeding pass, and aggressive following. The overall prediction accuracy is 95.1% for the single vehicle case and 96.2% for the following vehicle case.

scholarly journals DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision

2018 ◽  
Author(s):  
Hanyuan Zhang ◽  
Hao Wu ◽  
Weiwei Sun ◽  
Baihua Zheng
Keyword(s):  
Travel Time ◽  
Deep Neural Networks ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Urban Mobility ◽  
Time Cost ◽  
Trajectory Data ◽  
Estimation Model ◽  
New Development ◽  
Supervision Model

Estimating the travel time of a path is of great importance to smart urban mobility. Existing approaches are either based on estimating the time cost of each road segment which are not able to capture many cross-segment complex factors, or designed heuristically in a non-learning-based way which fail to leverage the natural abundant temporal labels of the data, i.e., the time stamp of each trajectory point. In this paper, we leverage on new development of deep neural networks and propose a novel auxiliary supervision model, namely DeepTravel, that can automatically and effectively extract different features, as well as make full use of the temporal labels of the trajectory data. We have conducted comprehensive experiments on real datasets to demonstrate the out-performance of DeepTravel over existing approaches. 

scholarly journals RealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks

2022 ◽  
Author(s):  
Qibin Zhou ◽  
Qingang Su ◽  
Dingyu Yang
Keyword(s):  
Travel Time ◽  
Real Time ◽  
High Speed ◽  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Trajectory Data ◽  
Real Time Traffic ◽  
Histogram Estimation ◽  
Traffic Estimation

Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.

Stop-and-Go Decision-making Mechanism Analysis of E-bikes During Signal Change Interval

2021 ◽  
Vol 22 (7) ◽  
pp. 1661-1672
Author(s):  
Sheng Dong Sheng Dong ◽  
Jibiao Zhou Sheng Dong ◽  
Shuichao Zhang Jibiao Zhou ◽  
Lin Guo Shuichao Zhang ◽  
Zhijian Wang Lin Guo
Keyword(s):  
Decision Making ◽  
Mechanism Analysis ◽  
Stop And Go ◽  
Signal Change

Prioritizing Green HRM practices from policymaker’s perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mehrajunnisa Mehrajunnisa ◽  
Fauzia Jabeen ◽  
Mohd Nishat Faisal ◽  
Khalid Mehmood
Keyword(s):  
Decision Making ◽  
United Arab Emirates ◽  
Past Research ◽  
Organizational Level ◽  
Service Firms ◽  
Corporate Environmentalism ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Management Orientation ◽  
Green Human Resource Management

Purpose This study aims to identify and prioritize Green human resource management (GHRM) practices from the policymaker’s perspective in the United Arab Emirates (UAE)-based manufacturing and service sectors to facilitate sustainable environmental performance. Design/methodology/approach Drawing upon the ability–motivation–opportunity (AMO) and corporate environmentalism theory, this study uses the analytic hierarchy process (AHP), a multi-criteria decision-making model, to rank the most influential enablers of GHRM practices. Data were collected from 24 C-suite executives of UAE-based manufacturing and service units. Findings Top management orientation for Green, Green organizational culture and Green corporate strategic planning were the most critical enablers that promote GHRM practices in the UAE’s manufacturing and service firms. Past research has mostly overlooked the strategic variables and focused only on organizational level antecedents based on HR bundles of practices. Research limitations/implications Data were collected only from UAE firms, hence limiting its generalizability. The study shall help organizations operating in emerging countries adopt the best GHRM practices toward Green goal agendas. Originality/value This research provides an AHP framework that can be used to conceptualize and prioritize GHRM practices, which aids in a firm’s Green decision-making and transition toward sustainable Green growth. This study furthers understanding of GHRM practices play out at the various levels-of-analysis within organizations to present a comprehensive paucity of integrative and multi-level studies over recent years. The study may be relevant for other organizations in other national contexts with similar governance homogeneity.

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