Transportation Letters-The International Journal of Transportation Research最新文献

Advancement of mobile technologies has enabled economical collection, storage, processing, and sharing of traffic data. These data are made accessible to intended users through various application program interfaces (API) and can be used to recognize and mitigate congestion in real time. In this paper, quantitative (time of arrival) and qualitative (color-coded congestion levels) data were acquired from the Google traffic APIs. New parameters that reflect heterogeneous traffic conditions were defined and utilized for real-time control of traffic signals while maintaining the green-to-red time ratio. The proposed method utilizes a congestion-avoiding principle commonly used in computer networking. Adaptive congestion levels were observed on three different intersections of Delhi (India), in peak hours. It showed good variation, hence sensitive for the control algorithm to act efficiently. Also, simulation study establishes that proposed control algorithm decreases waiting time and congestion. The proposed method provides an economical alternative to expensive sensing and tracking technologies.

Synchronizing air and intercity high-speed railway (A-IHSR) services has recently been developing vigorously. Train timetables are critical for improving A-IHSR synchronization. This paper proposes a method to optimize intercity high-speed railway (IHSR) timetables based on the dynamic passenger demand of the A-IHSR. An integer non-linear programming (INLP) model is formulated to minimize the passenger waiting time, in which rigorous train capacity, oversaturation situation, and skip-stop are also taken into consideration. We also extend the train timetabling model by considering the synchronization events of flight-train and the train circulation plan. Then, this model is reformulated as a relaxed non-linear programming (NLP) model.. A multimodal nomad algorithm (MNA) is proposed to solve it. The train circulation plan solves by the Gurobi solver. A numerical experiment based on data of the operation of the Jinnan–Binzhou HSR is implemented to demonstrate the efficiency and performance of our proposed model and algorithm.

As a green travel mode, public bikes have positive implications in terms of reducing emissions. Based on the life cycle theory, we calculate the carbon emissions generated and reduced over the entire life cycle of public bikes.The calculation shows that when the average daily turnover rate of public bikes is 1.874 times/ bike, the average daily travel distance is 2.150 km, and the damage rate increases by 2.5% per month, each public bike needs approximately 7 months to reach the carbon balance. After the carbon emission balance is reached, the use of public bikes causes a net reduction in carbon emissions. However, the carbon emissions once again exceed the emission reductions after approximately 29 months of using public bikes. Furthermore, the carbon balance of the 186 stations used in Wenling City is studied.Based on the conclusions obtained, some policy recommendations are made to help public bikes achieve real emissions reduction.

Comparison of routes recommended by online trip planners, such as Google Maps and MapQuest, with routes traveled by cyclists allows estimation of whether the routing criteria used in such trip planners are the same as those used by cyclists. This study uses GPS tracking data from the Endomondo fitness tracker app from trips in Miami-Dade (Florida) and North Holland and compares their characteristics to cycling routes suggested by Google Maps, MapQuest and the shortest path. Results highlight which trip attributes differ between Endomondo and its alternative trips, and how consistent those differences are across the two study areas. For example, Endomondo cyclists in both regions go through fewer traffic signals, and use more cycleways, footways, or bike lanes than suggested routes from Google and MapQuest. A multinomial logit model on observed Endomondo trips provides insight into the routing preferences of Endomondo commute and sport cyclists in both study regions.

Vehicle Acceleration/Deceleration (A/D) characteristics are fundamental measures often used in designing intersection geometrics, A/D lanes, analyzing vehicle fuel consumption and emission, and traffic simulation modeling. The flow scenario at a signal-controlled intersection involves stop-and-go conditions with variability of A/D characteristics. This study attempts to analyze the vehicle’s A/D behavior at an urban signalized intersection under weak lane disciplined mixed traffic stream using vehicle trajectory data. The result shows that vehicles like car and motorized two-wheeler are associated with high acceleration behavior at lower speeds and need less time and distance to accelerate. Also, these vehicles can decelerate at a higher rate at a lower speed during the stop or clearing of the intersection. The statistical results show that a single and multi-regime polynomial model is more suitable to represent the behavior of A/D characteristics in terms of the approach speed of vehicles.

Unlike lane-based traffic where each driver has a distinct leader, the subject driver in disorderly traffic may interact with multiple vehicles in-front. The existence of lateral interactions among the vehicles in-front adds even more complexity to modeling the human-driving process. Utilizing trajectory data extracted from an instrumented vehicle study, this research attempts to propose a gated recurrent unit neural network model to predict responses of vehicles interacting with two leading vehicles simultaneously. The recurrent neural network model can illustrate realistic human-like following behavior of drivers, much better than the classical optimal velocity-based models in terms of trajectory reproducing accuracy. The model can also explain the closing-in, shying-away behavior and local stability properties. Results of the study provide insights into the driving behavioral phenomena of disorderly traffic flows and can contribute to the development of a realistic microsimulation model, smarter autonomous systems, and in-traffic safety evaluation as well.

One specific phenomenon in the Spring Festival travel rush in China is that purchasing a train ticket becomes tough as the train tickets could be sold out extremely soon. Therefore, the current paper presents an analysis of college students’ trip ticket purchasing strategies for homecoming trips during Chinese Spring Festival using a sequential stated adaptation experiment, based on which three different choice models were applied and the results show that 1) college students would keep purchasing the ticket of planned train trip and this willingness would decrease with the increasing of the times they fail to get the ticket; 2) if college students decide to give up the conventional train trip they have planned, they have a tendency to choose other conventional train trips, but it is not the case regarding high-speed train trips; 3) air itinerary trips are attractive alternatives to trains trips, especially to high-speed train trips.

This paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. Specifically, this study investigates the use of drone raw data at a busy three-way signalized intersection in Athens, Greece, and builds on the open data initiative of the pNEUMA experiment. Using a microscopic approach and shockwave analysis on data extracted from real-time videos, we estimated the maximum queue length, whether, when, and where a spillback occurred, vehicle stops, vehicle travel time and delay, crash rates, and fuel consumption. The results of the various MOEs were found to be promising. We also demonstrated that estimating MOEs in real-time is achievable using drone data. Such models can track individual vehicle movements within street networks and thus allow the modeler to consider any traffic conditions, ranging from highly under-saturated to highly over-saturated conditions.

Motorized Two Wheelers (MTWs) possess specific driving patterns which increase their crash risk, especially in heterogeneous traffic, typically found in developing countries. Understanding the driving behavior of MTWs in such conditions is crucial for transportation researchers and vehicle manufacturers to improve driving behavior modeling, vehicle design, and risk evaluation. This study aims to model single-leader following behavior of MTWs in mixed traffic, by using Generalized Linear Modelling (GLM) technique to develop a safe-distance following model considering various factors specific to mixed traffic. Time to Collision (TTC) and Potential Index for Collision with Urgent Deceleration (PICUD) were employed for the safety assessment. Results of the study present a typical MTW following behavior and associated crash risk for heterogeneous traffic. Apart from theoretical applications such as microsimulation modeling, this study is useful for smart systems such as collision avoidance systems, in-vehicle warning and autonomous driving systems.

This study introduces a new policy for managing parking scarcity called Parking Allocation and Ride-Sharing System (PARS). In PARS, a centralized algorithm allocates parking spacesto drivers who are willing to participate in a coordinated carpool. The algorithm is used to optimize the creation of carpools going to and returning from a particular venue and simultaneously reserve parking for these carpools at the venue. An efficient mixed integer linear programming (MIP) formulation is presented and two heuristics, namely Ride Decomposition (RD) and Quick Converge (QC), are proposed and compared via internally generated experiments. Experimental results show that a commercial solver is able to solve the MIP with thousands of individuals to optimality in minutes. For larger instances, the RD and QC heuristic algorithms can solve the problem, on average, 42.23% and 86.39% faster than the commercial solver and provide solutions that are 3.61% and 3.49% from optimal, respectively.