Journal of Transportation Engineering, Part A: Systems

As ride-sharing emerges as a new mobility choice, modeling and control of large-scale multimodal systems in automated and shared environment recently has received significant attention. This paper developed a modeling approach for capturing the demand–supply relationship and the traffic flow dynamics in such ride-sharing systems at an aggregated network level. Private cars, taxis, and single- and multioccupancy ride-sharing vehicles are considered. A macroscopic fundamental diagram–based traffic flow model was constructed to describe the spatiotemporal physics of traffic. Multimodal meeting functions are utilized for passenger–vehicle matching in the shared transportation system. The proposed model enabled us to formulate an optimization model for region-level dispatching control strategies and relocating taxis and ride-sharing vehicles for different objectives. The results of experimental study reveal that (1) the proposed model can reproduce traffic dynamics and multimodal interactions under various traffic conditions, travel demands, and service intensities; and (2) the developed control theory–based dispatching strategies can improve the efficiency of all modes in the shared transportation system, and reduce the travel cost for riders and promote the level of service for passengers.

In recent years, many researchers have paid great attention to the transportation convenience and advantages brought by the future extensive use of connected and automated vehicles (CAVs). However, CAVs will degrade to lower-rank automated vehicles (AVs) when vehicle-to-vehicle (V2V) communication links are not available, which will cause a mess of traffic and even increase the risk of collision. How to avoid hard degradation of CAVs or to maintain the cooperative status based on the AVs information will be a highly concerning problem worth studying. This paper proposes a soft degradation strategy, in which the degraded CAVs will keep cooperative control only based on historical information of AVs. Specifically, the strategy uses historical information detected by onboard sensors to infer the acceleration of the preceding vehicle. Theoretical analysis shows that the proposed soft degradation strategy can significantly improve traffic flow stability caused by the degradation of CAVs. The direct numerical results are in good agreement with those of theoretical analysis. Compared with the existing strategies, our strategy can better improve traffic stability, safety, and fuel economy when CAVs degrade to AVs. These findings can give insights for traffic managers and vehicle designers to solve the degradation of CAVs.

Faced with the urban sprawl and the increasingly serious urban traffic problems caused by the sustained and rapid economic growth and fast development of urbanization, advocating the development of metro-led cities and intensive and compact development to form a green, transportation-oriented built environment has become a national strategic need. In order to promote the integrated development of stations and the city, and further enhance the vitality of the city, the concept of metro microcenter transportation was brought up in Beijing. A metro microcenter is a geographical space with full integration and interaction with metro stations, high accessibility, high degree of intensive land use, and multiple urban functions. Considering the characteristics of metro microcenters, the mechanism between passenger flow and the built environment in the general control and management area of a metro microcenter is worth studying. However, few previous studies have systematically and comprehensively carried out theoretical and empirical research on the relationship between the built environment and multimodal public transport passenger flow characteristics around metro station catchments. Based on the 500-m metro core station catchment, this study introduced the data envelopment analysis (DEA) method, and a highly efficient and refined public travel efficiency assessment model (Bootstrap-Super-DEA) was proposed to evaluate the transit-oriented development (TOD) level of the existing metro core station catchment. Metro station passenger flow, bus station passenger flow, and shared-bicycle passenger flow were selected as output variables of this model. Network centrality, functional mix, and other building environment indicators were selected as input variables of this model. In addition, the derived indicators of passenger flow were extracted to reveal the relationship with efficiency from the spatiotemporal distribution characteristics. The model effectively distinguishes the distribution of metro core station catchments with efficient TOD. The study results can provide a reference for metro microcenter planning managers to grasp the core station catchment’s development tone accurately.

Data-driven traffic modeling is revolutionizing transportation systems and provides numerous opportunities for achieving high-quality transportation services. A major challenge in optimizing transportation systems is uncertain transportation demand. With the availability of historical data on transportation demand, the uncertain transportation demand can be better modeled, and thereby practitioners can formulate well-informed transportation scheduling decisions. In this paper, we propose three effective and economical transport scheduling strategies using mathematical programming, leveraging big data to extract useful contextual information. Additionally, a perfect-foresight optimization model is proposed to evaluate our proposed data-driven strategies. Results show a negligible optimality gap (i.e., 0.47%) between the optimal solution derived by the perfect-foresight model and the scheduling plans derived by our data-driven strategies. Overall, this paper contributes to the field of transportation engineering by innovatively applying data science, mathematical modeling, and optimization techniques.

Designing and optimizing auxiliary roads to support railway construction in complex mountainous areas is challenging. This paper presents a two-stage optimization model for auxiliary road design. The first stage designs the network layout of auxiliary roads based on the minimum spanning tree method to reduce link costs among various railway auxiliary construction projects. The second stage involves designing and optimizing the alignment of the auxiliary roads based on the deep reinforcement learning approach to minimize construction costs. In addition, the different logistical relationships among various railway auxiliary construction projects are considered to optimize the total turnover volume of the auxiliary road. Finally, a real-world case study of auxiliary road design for a railway construction project in mountainous areas was conducted to verify the proposed method.

There are many vulnerable road users on urban roads, such as pedestrians and personal mobility users. Additionally, there are various risky roads on urban roads, including signalized intersections and school zones. Speed management is one of the strategies for improving traffic safety, and speed-reduction pavement marking is one of the traffic-calming facilities that can produce the desired deceleration effect at a relatively low cost for safety management. In this study, for multilateral analysis to adopt the optimal marking on urban roads, first, several pavement markings were investigated through both domestic and foreign cases. And speed-reduction pavement markings that are proper to install on the urban road and show significant speed-reduction effects were selected. Then, the effect of each pavement marking was evaluated from the survey analysis and driving simulator experiment. The optimal marking design was selected through the first survey and the driving simulation results with a second survey after the experiment. Finally, empirical evaluation in the self-driving experimental area (K-City) was performed to explore the speed-reduction effect of the selected pavement marking. The results showed that the chosen marking demonstrated its effectiveness for inducing deceleration, and also it can adjust the degree of speed reduction of heterogeneous road sections to a similar level. Therefore, we expect that the selected pavement marking design can reduce speed on the urban road and it will contribute to enhancing drivers’ speed limit compliance rate following the lowering of the urban speed limit according to the new policy.

As one of Tibet’s most vital traffic safety devices, guide signs are usually Tibetan–Chinese bilingual. Therefore, drivers need effective recognition of Tibetan–Chinese bilingual traffic signs for safe driving. This study aims to determine two design parameters of the Tibetan–Chinese bilingual guide signs: Tibetan character height ($h_T$) and aspect (height to width) ratio. Two highway facilities were selected for the simulation experiment: a general highway intersection and a high-grade highway exit ramp. To incorporate drivers’ visual recognition characteristics, 20 Tibetan drivers and 20 Han drivers were invited to wear head-mounted eye trackers for a dynamic driving simulation experiment of visual identification of guide signs. Four layout designs were used to analyze Tibetan and Han drivers’ visual recognition characteristics. The regression model for $h_T$ was first developed based on the visibility theory of guide signs. Then, the character-height design values for the Tibetan–Chinese bilingual guide signs were established for different speed limits. The results of this study should help to optimize the design of traffic signs in Tibet and improve highway traffic safety.

This study presents a methodology in a disaggregated manner to assess the spatial distributional effects of implemented toll road networks. Existing zone-based research focuses only on revealing the existence of regressivity. This approach provides limited information in understanding the spatial distribution of travelers from low-income groups known to have suffered welfare loss due to toll roads. For road pricing to be a win-win strategy for all income groups, the revenue from toll roads should be used to improve the mobility of low-income groups. Therefore, it is desirable to have more detailed location information and regressivity effects for each income group due to toll roads. This study proposes a new method to better represent the spatial distributional effects by utilizing disaggregated and recently available data (e.g., smartphone navigation data), enabling analysts to easily estimate the welfare loss of low-income groups due to toll roads. Applying the proposed method in Chungcheongnam-do, Korea, reveals that installing toll roads would be regressive in terms of income. However, the analysis results provide sufficient information to interpret spatial distributional effects’, current status, and causes. This method improves the existing zone-based analysis, which cannot distinguish who has greater welfare loss among high- and low-income groups in a particular zone. This information would be extremely useful in establishing mitigation policies for low-income groups affected by installing toll roads in the future.