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

March 2020 will forever be etched in our minds as the beginning of the most concerning health pandemic faced by all generations of the living population. Two-and-three quarter years on, we are starting to see signs for what the future might evolve into through structural change brought about by many events, and no more so than the burgeoning growth in working from home (WFH). WFH is no longer associated with negative stigma, and along with remote working more generally, has become recognised across most sectors of society as a way of work that has benefits for many and is to some extent here to stay. We draw on the research undertaken since March 2020 to summarise the evidence that we use to speculate on what are likely to be the big changes in the land transport sector that would not have been considered, at least to the same extent, pre-COVID-19.

Travelers always perform some preference during the decision-making process. The preference will affect the decision results and can be improved by continuously learning. In order to understand the influence of individual preference on travel behavior choice , two individual preferences, including indifference preference and compulsive preference are considered in the paper. Two updating mechanisms of compulsive preference are proposed to obtain the choosing probability of all alternatives. Reinforcement learning models are established integrating the gain stimulating and loss stimulating considering expected utility. Nguyen Dupuis network is adopted for numerical simulation to study the updating process. Simulation results denote that the equilibrium state is much more efficient when preference learning mechanism is considered comparing with the traditional stochastic user equilibrium model, and can decrease the total travel time greatly, which can be applied for urban traffic management. Personalized traffic guidance is the effective solution to traffic congestion in the future

In the logistics industry, unmanned aerial vehicles (UAVs) are mostly used for last-mile delivery, in combination with other types of vehicles. There is currently no operator in Taiwan that is completely reliant upon the usage of UAVs for cargo delivery services. Therefore, this study proposes a routing and scheduling model for UAVs by utilizing the network flow technique and mathematical programming methods. All advance requests must be satisfied, and the related operating constraints ensured in the model. The model aims to minimize the total operating cost. To effectively solve large problems that may occur in practice, this study develops a relax-and-fix heuristic. Numerical tests are conducted to preliminarily examine whether the model, coupled with the heuristic algorithm, could be applied in practice. The test results indicate that the proposed model and solution algorithm are effective and thus could be useful for UAV operators to perform delivery routing and scheduling.

This paper proposes a capacity model to address the impact of automated dedicated lanes on the capacity of the signalized intersection. Firstly, the car-following modes in the mixed traffic flow are analyzed, and the influence of the setting of the automated dedicated lanes on the average headway is discussed. Secondly, a new capacity model with automated dedicated lanes is derived based on the classic capacity model. Then, a signalized intersection capacity model considering the automated dedicated lanes is further derived based on the saturation flow rate method. Finally, numerical simulation experiments are designed to discuss the key parameters on the traffic capacity of a signalized intersection. The results show that (i) the automated dedicated lanes are conducive to improving the traffic capacity; (ii) when the penetration rate of CAVs is less than 52%, the traffic capacity of the signalized intersection can be increased by nearly 1.25 times with the automated dedicated lanes. These findings can provide theoretical support for designing and optimizing signalized intersections in high levels (i.e. L3-L5) CAVs environments.

This paper focuses on the joint optimization of the charging station location problem (CSLP) and discrete network design problem (DNDP) in a transportation network. We present a variational inequality (VI) formulation to describe the user equilibrium (UE) state of gasoline vehicles (GVs) and electric vehicles (EVs). Based on the mixed-UE model, a mathematical program with equilibrium constraints (MPEC) model is formulated for integrating the decisions of deploying EV charging stations (EVCSs) and adding new links to minimize the total travel cost (TTC) of all vehicles. A modified genetic algorithm is developed to tackle the MPEC model with an adaptive path generation procedure to address the mixed-UE model. Finally, we conduct numerical experiments to identify the efficacy of the proposed models and algorithms. Specifically, we propose a two-step optimization model and explore a performance comparison between the joint and two-step optimization approaches, while the joint optimization exhibits superiority in minimizing the TTC.

Overtaking time in two-lane undivided rural highways is vital for traffic safety and operations. This study used the hazard-based duration models to investigate the 117 accelerative overtaking maneuvers along a 44-km-long National Highway 61 in India. The data were collected for 28 drivers using an instrumented passenger car (PC) overtaking different vehicle classes such as motorized three-wheeler (M3W), PC, light commercial vehicle (LCV), and heavy vehicle (HV). The log-logistic distribution better represented the model for all overtaken vehicle classes. The survival and hazard function plots were developed for each vehicle class, and the observed inflection points were compared. The likelihood of overtaking was maximum at 9.2, 10.2, 9.6, and 11.8 sec for M3W, LCV, PC, and HV, respectively. The observed results can help evaluate overtaking opportunities based on overtaken vehicle class, better estimation of percentage time spent following for the level of service evaluation of two-lane undivided highways with mixed traffic streams.

Sustainable transport policies are fundamental to adapt transport capacity to existing and future travel demand. In this context, this study aims to develop a System Dynamics (SD), to verify the effects of these policies, focusing on congestion and air pollution. Combined with the SD, the discrete choice utility approach was used to predict the modal share in different policy spaces. In addition, it is carried out a case study in Rio de Janeiro in two realities: pre- and post-pandemic. The results show how mitigation policies can reduce transport externalities (congestion and pollution). The encouragement of high-capacity public transport and car ownership control are the best measures, obtaining high simulation scores. The post-pandemic scenario shows that reducing travel demand is the key to achieving better results. All scores obtained in this scenario are better than in pre-pandemic scenario. Finally, results point out that ride-hailing should be used in a conscious way.

We present a novel ramp metering algorithm that incorporates multi-agent deep reinforcement learning (DRL) techniques, which utilizes monitoring data from loop detectors. Our proposed approach employed a multi-agent DRL framework to generate optimized ramp metering schedules for each ramp meter in real-time, enhancing the operational efficiency of urban freeways with less investment. To simplify the implementation and training of the algorithm, we developed a simulation platform based on SUMO microscopic traffic simulator. We conducted a series of simulation experiments, including local and coordinated ramp metering scenarios with various traffic demands profiles. The simulation results indicate that the proposed DRL-based algorithm outperforms the state-of-the-practice ramp metering methods, considering a comprehensive evaluation index encompassing mainstream speed at the bottleneck and queue length on ramp. Additionally, the method exhibits robustness, scalability, and the potential for further improvement through online learning during implementation.

Multi-vehicle anticipation (MVA) refers to drivers’ ability to consider stimuli from several vehicles ahead in their maneuvering decisions, such as longitudinal, lateral, and a combination of longitudinal and lateral movements. This paper provides a comprehensive review of MVA-based driver behavior models developed for both homogeneous and heterogeneous disordered (HD) traffic streams. Studies on MVA identify various advantages of incorporating MVA in driver behavior models, such as superior numerical and behavioral soundness, plausible parameter estimates, and model outputs, and improved model realism. In addition, our findings indicate that MVA-based driver behavior models follow a similar pattern of extending the established single-leader car-following models, considering vehicles that are directly ahead (in the same lane), and focussing on a fixed number of vehicles ahead. For HD traffic streams, drivers’ also consider stimuli from vehicles obliquely placed or on either side. Furthermore, this review discusses issues with the current modeling approaches and suggests future research directions

This study explored the effectiveness of various employer-based travel demand management strategies in promoting multimodality and mode substitution among employees in Washington state using a mixed multinomial logit model. The study found that employee transportation coordinators played an important role in encouraging the use of sustainable travel modes. Spatial analysis revealed that individuals who lived and worked in proximity were more likely to adopt multimodal transportation. The study also highlighted the convenience of driving alone and the lack of information on sustainable alternatives as two major barriers to the adoption of sustainable transportation modes and recommended educational campaigns to increase awareness. To inform practice, this study identified transit subsidies, parking pricing, and work schedule flexibility as the most effective TDM strategies to promote multimodality and mode substitution, followed by compressed workweeks, and providing easy access to transit and amenities.