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

Travel times in real-world transportation networks are affected by many disruptions. When we conduct the network design optimization, the traffic condition and its resulting travel time variability should be taken into account. However, most of the previous network design optimizations adopted the lengths or expected travel times of links. Based on travel time means and standard deviations, we develop an arc-based model that is a nonlinear and concave integer program. By the Dantzig-Wolfe reformulation, we transform it into an equivalent column-based model that is an integer linear program with a large number of variables. Based on the column-based model, we develop a hybrid method based on column generation and Lagrangian relaxation. The restricted master problem can be settled by the linear programming solvers. The pricing subproblems incorporate independent reliable shortest path problems and a knapsack problem. In numerical experiments, the proposed method can generate feasible solutions with good integrality gaps.

Most traffic conflict indicators are defined for car-following scenarios where a follower vehicle interacts with a leader vehicle in one-dimensional space. However, vehicles do interact in a two-dimensional space especially in a heterogeneous traffic environment. Further, designating an interaction as risky depends on the interacting leader-follower (LF) pairs.

Conflict indicators namely Time-to-Collision (TTC) and lateral gap which quantifies longitudinal and lateral interactions respectively, were computed from video recordings at four accident black spots on four-lane divided highways. Conflict in two-dimensional space was modelled for various LF-pairs using the Bivariate Extreme Value function of these two conflict indicators. Crash risk was estimated for each LF-pairs separately. Results show that cars and light commercial vehicles exhibit higher crash risk as compared to two-wheelers and motorized three-wheelers. The proposed framework can be used for more accurate risk assessment and calibration of collision warning systems in lane free mixed traffic conditions.

As an affordable form of public transit, the metro still faces first- and last-mile problems. Integration of dockless bike sharing (DBS) and the metro offers a reasonable solution to this problem. However, an in-depth exploration of the spatio-temporal characteristics and determinants of DBS–metro integration, especially meteorological indicators, is lacking. The spatiotemporal characteristics of the transfer volume on weekdays and weekends in Beijing were analyzed. Geographically weighted regression was utilized to investigate the effects of the significant determinants on the spatial variation of the transfer volume. The results indicate that the transfer volume is concentrated within the Fifth Ring Road in Beijing. The effects of education services and metro station density were negatively correlated with transfer volume. As expected, population density and enterprises positively affect transfer volume, whereas wind speed and relative humidity have negative effects. These findings provide new insights into the promotion of DBS–metro integration.

Countdown timers impact driver behavior significantly and are widely used at signalized intersections in many countries. Literature shows potential vehicle class-specific temporal and spatial variations in driver behavior in the presence of timer. Therefore, first, this paper analyzed the temporal, spatial, and vehicle class-specific impacts of timer on driver behavior in heterogeneous traffic conditions based on variations in acceleration. Second, a Long Short-Term Memory model was developed to model vehicle discharge, incorporating the heterogeneity of traffic and impacts of timer. The performance of the model was compared with that of the Intelligent Driver Model using field trajectory data from three signalized intersections in Kerala, India. The model accurately predicts the following behavior with mixed spacing errors of 0.24 to 0.35 and an average mean squared error of spacing of 1.23. The results were better than, or comparable to, results from recent data-driven car-following models and significantly better than IDM.

.The present study aims to calibrate a novel multi-class non-lane-based continuum traffic flow model by applying advanced global search optimization algorithms such as Hybrid Search (Combination of genetic algorithm and Nelder Mead) and Generalized Pattern Search. To represent vehicular behavior in non-lane-based mixed traffic environment, the new continuum model is deduced from two-sided lateral gap car following theory along with few empirical observations. The newness of the selected model is that it mimics complex overtaking behavior of vehicles, effect of slow-moving vehicles on traffic stream and driver’s anticipation behavior. The search algorithms considered in this study have been tested and compared using the real-world data. From the analysis, it is observed that the proposed algorithms are more accurate in minimizing the cost function whereas convergence speed is also found to be better. The calibrated parameters are validated using platoon dispersion characteristics of vehicles and alternative transport policy measures.

This paper aims to make contributions to balancing the passenger flow among high-speed railway trains of common lines as well as raise revenue via a behavioral-based optimal dynamic pricing. Based on an HSR trip choice survey, a set of Nested-logit (NL) models is built to analyze passengers’ preferences for different factors under various scenarios. The upper level belongs to the departure time period while the lower level belongs to specific trains with different levels of service. Given the conditions of fixed transportation capability, an optimization model is built to optimize the pricing among trains of common lines. The NL model results show that the time values of round trips are greater than that of one-way trips, and the time values of business trips are greater than that of leisure trips. Besides, the optimization model is verified to be capable of raising revenue, improving passenger flow equilibrium, and inducing extra passenger demand.

A significant portion of rural crashes in Iran occur in the marginal areas around cities. Exclusive crash frequency models should be developed to identify the factors affecting the occurrence of crashes in these areas. In this study, the rural crash data of highways leading to the city of Isfahan were employed to develop a negative binomial (NB) model as a constant-parameter model, along with random effects negative binomial (RENB) and random parameters negative binomial (RPNB) models as the models based on the random parameter approach. The modeling results indicated the superiority of the RPNB model over the two other models. According to the findings of this study, the traffic volume of roads and the proportion of local and non-local vehicles, the proportion of longitudinal distance-limit violation, the way of controlling access points, and the access to rest areas affected the crash frequency in the marginal areas around cities.

In the wake of the COVID-19 pandemic, scholars mobilized their efforts to address its far-reaching societal problems. With mobility restrictions being front and center of the pandemic, a new cohort of transportation science was developed within a short period of time. Here, we examine more than 400 studies related to COVID-19 published across transportation journals during 2020 and 2021. The aim is (i) to scope this newly developed segment of transportation research, (ii) outline the diversity of pandemic-related issues across various divisions of the transportation field and (iii) provide a roadmap for the future of this line of research. Common themes are identified and existing congruence and discrepancies across findings are discussed. Results show that although conventional methods of transportation research were adopted in virtually all COVID-19 studies, no pre-pandemic study was particularly instrumental in the development of this segment of transportation literature. The COVID-19 segment appears to have developed its own independent knowledge foundation, in that, it does not systemically and frequently look back at any particular pre-pandemic reference. Potential impacts of this newly developed segment on the metrics of transportation journals are quantified and discussed.

This study characterizes lane changing behavior of drivers under differing congestion levels and identifies extreme lane changing traits using high-resolution trajectory data. Total lane change frequency exhibited a reciprocal relationship with congestion level, but the distribution of lane change per vehicle remained unchanged as congestion increased. On average, the speed of trajectories increased by 5.4 ft/s after changing a lane. However, this gain significantly diminished as congestion worsened. Further, the average speed of lane changing vehicles was 3.9 ft/s higher than those that executed no lane changes. Two metrics were employed to identify extreme lane changing behavior: critical time-to-line-crossing (TLC^c) and lane changes per unit distance. The lowest 1% TLC^c varied between 0.71–1.57 seconds. The highest 1% of lane change rates for all lane changing vehicles was 2.5 lane changes per 1,000 ft traveled. Interestingly, no drivers in thisdataset had both excessive lane changes and lane changes with low TLC^c.