Transportation Research Part C-Emerging Technologies最新文献

The use of Air traffic management (ATM) simulators for planing and operations can be challenging due to their modelling complexity. This paper presents XALM (eXplainable Active Learning Metamodel), a three-step framework integrating active learning and SHAP (SHapley Additive exPlanations) values into simulation metamodels for supporting ATM decision-making. XALM efficiently uncovers hidden relationships among input and output variables in ATM simulators, which are usually of interest in policy analysis. Our experiments show that XALM’s predictive performance is comparable to that of the XGBoost metamodel with fewer simulations. Additionally, XALM exhibits superior explanatory capabilities compared to non-active learning metamodels.

Using the ‘Mercury’ (flight and passenger) ATM simulator, XALM is applied to a real-world scenario in Paris Charles de Gaulle airport, extending an arrival manager’s range and scope by analysing six variables. This case study illustrates the effectiveness of the proposed framework in enhancing simulation interpretability and understanding variable interactions. By addressing computational challenges and improving explainability, it complements traditional simulation-based analyses.

Lastly, we discuss two practical approaches for reducing the computational burden of the metamodelling further: we introduce a stopping criterion for active learning based on the inherent uncertainty of the metamodel, and we show how the simulations used for the metamodel can be reused across key performance indicators, thus decreasing the overall number of simulations needed.

The use of videos for effective crash and near-crash prediction can significantly enhance the development of safety countermeasures and emergency response. This paper presents a two-stream hybrid model with temporal and spatial streams for crash and near-crash identification based on front-view video driving data. The novel temporal stream integrates optical flow and TimeSFormer, utilizing divided-space–time attention. The spatial stream employs TimeSFormer with space attention to complement spatial information that is not captured by the optical flow. An XGBoost classifier merges the two streams through late fusion. The proposed approach utilizes data from the Second Strategic Highway Research Program Naturalistic Driving Study, which encompasses 1922 crashes, 6960 near-crashes, and 8611 normal driving segments. The results demonstrate excellent performance, achieving an overall accuracy of 0.894. The F1 scores for crashes, near-crashes, and normal driving segments were 0.760, 0.892, and 0.923, respectively, indicating strong predictive power for all three categories. The proposed approach offers a highly effective and scalable solution for identifying crashes and near-crashes using front-view video driving data and has broad applications in the field of traffic safety.

Rapid urban growth and consequent increase in e-commerce demand make urban logistics a harder task than ever. The growing size of urban delivery operations not only entails operational challenges but also generates several negative externalities, such as increased traffic, pollution, noise, and accidents. This trend creates a pressing need for efficient delivery mechanisms that are more economical and environmentally friendly than existing systems. Crowdshipping, wherein ordinary members of the society partake in delivery operations for a small compensation, is one of the answers that cater to this need and has attracted considerable research interest recently. However, designing compensation mechanisms to prompt efficient participation from the public remains largely unexplored in the literature. In this study, we devise a dynamic compensation scheme for crowdshipping operations in a many-to-many express delivery framework, where the crowdshipper compensations are determined based on spatial and temporal distributions of the delivery demand and continually updated during the service time to leverage the crowd participation as needed. To address the resulting complex network management problem, we derive analytical solutions for compensation optimization and use these results along with effective pruning strategies to build a lookup table to simultaneously determine package routes and compensation offers in real time. Computational studies and extensive simulations conducted with real-world data show that our proposed approach can provide significant cost savings and considerably reduce operational costs and other transport-related negative externalities when compared to classical delivery modes, crowdshipping with static compensations, and crowdshipping without transshipment.

Aviation is one of the global warming contributors. Its impact is due to CO${}_2$ and non-CO${}_2$ effects. Trajectory design is one of action levers for minimizing the environmental impact of air transportation. However, it affects the Air Traffic Management and should satisfy airspace constraints, especially airspace capacities. This paper proposes a climate-aware version of the Air Traffic Flow Management (ATFM), focusing on CO${}_2$ and one particular non-CO${}_2$ effect: condensation trails (contrails), although other non-CO${}_2$ effects can be integrated. A deterministic ATFM optimization model is proposed, solved by a column generation approach. This problem is solved using different metrics, from simple to more complex and realistic ones. Numerical experiments are conducted both in the lateral case and when the cruise altitude becomes a decision variable. The impact of airspace capacities is also evaluated. The problem instances that are studied are built from realistic open-access data and made publicly available.

Shared private parking spots (SPPSs) are believed to play a crucial role in balancing the surging parking demand and further alleviating the parking pressure of public carparks. Previous relevant studies mainly focused on public parking spot management without consideration of SPPSs. Additionally, limited attention has been paid to the competitive behavior of parking options between two markets in traffic dynamic analysis in the literature. This paper investigates the parking supply and pricing strategies for a linear urban transportation network with both public and shared private parking spots. Firstly, we derive a user equilibrium in terms of departure time and mode-and-parking choice, with sufficient public parking spot provision. Secondly, we explore how the lack of public parking spots pushes some commuters to rent SPPSs and develop a mixed user equilibrium with an internal balance between SPPSs’ market demand and supply in case of inadequate public parking spots. Then, we further investigate how public parking spot provision and public parking fees affect commuters’ mode choice and departure time choice. Thirdly, we propose three management schemes: optimal public parking fee, optimal public parking spot provision, and joint optimization of public parking fee and public parking spot provision. Numerical results show that setting a proper public parking fee and providing sufficient public parking spot provision can effectively reduce total system cost. Moreover, the joint optimization scheme can further enhance the system performance in comparison to the other two management schemes.

Max-pressure (MP) is a decentralized adaptive traffic signal control approach that has been shown to maximize throughput for private vehicles. However, MP-based signal control algorithms do not differentiate the movement of transit vehicles from private vehicles or between high and single-occupancy private vehicles. Prioritizing the movement of transit or other high occupancy vehicles (HOVs) is vital to reduce congestion and improve the reliability and efficiency of transit operations. This study proposes OCC-MP: a novel MP-based algorithm that considers both vehicle queues and passenger occupancies in computing the weights of movements. By weighing movements with higher passenger occupancies more heavily, transit and other HOVs are implicitly provided with priority, while accounting for any negative impacts of that priority on single occupancy vehicles. And, unlike rule-based transit signal priority (TSP) strategies, OCC-MP more naturally also accommodates conflicting transit routes at a signalized intersection and facilitates their movement, even in mixed traffic without dedicated lanes. Simulations on a grid network under varying demands and transit configurations demonstrate the effectiveness of OCC-MP at providing TSP while simultaneously reducing the negative impact imparted onto lower occupancy private vehicles. Furthermore, OCC-MP is shown to have a larger stable region for demand compared to rule-based TSP strategies integrated into the MP framework. The performance of OCC-MP is also shown to be robust to errors in passenger occupancy information from transit vehicles and can be applied when passenger occupancies of private vehicles are not available. Finally, OCC-MP can be applied in a partially connected vehicle (CV) environment when a subset of vehicles is able to provide information to the signal controller, outperforming baseline methods at low CV penetration rates.

Transportation administrators and urban planners rely on accurate network-wide traffic state estimation to make well-informed decisions. However, due to insufficient sensor coverage, traffic state estimation at sensor-free locations (TSES) poses significant challenges for downstream network-wide traffic analysis. This is because direct observations are not available at these sensor-free locations. Most existing traffic state estimation (TSE) research focuses on inferring several unknown time points based on observed historical data using deterministic models. In contrast, TSES is to infer the entire unknown traffic time series of a given sensor-free node, thereby presenting high predictive difficulty, as we could not learn any historical traffic patterns locally. In this study, we introduce a novel probabilistic model — the conditional diffusion framework with spatio-temporal estimator (CDSTE) — to tackle the TSES problem. When dealing with TSES, deterministic models can only produce point value estimates, which may substantially deviate from the actual traffic states of sensor-free locations. To mitigate this, the proposed CDSTE integrates the conditional diffusion framework with cutting-edge spatio-temporal networks to extract the underlying dependencies in traffic states between sensor-free and sensor-equipped nodes. This integration enables reliable probabilistic traffic state estimations for sensor-free locations, which can be used to quantify the variability of estimations in TSES to support flexible and robust decision-making processes for traffic management and control. Extensive numerical experiments on real-world datasets demonstrate the superior performance of CDSTE for TSES over five widely-used baseline models.

Ridesharing occurs when people with similar schedules and itineraries travel together to reduce their commuting costs. In this paper, we study how parking spaces can be used to incentivize drivers to participate in ridesharing systems. We develop a Parking Incentive Allocation (PIA) system to promote and allocate parking lots to ridesharing drivers in a stochastic and dynamic environment. The optimization problem is formulated at each period as a multi-stage stochastic decision-dependent program. To overcome the complexity of the model, we propose one greedy policy, and three approximations including two stochastic policies and an expected-value policy. We evaluate the effectiveness of the four policies on the data generated from GPS information collected by the MTL Trajet project, which studies residents’ travel patterns throughout the city of Montreal. The computational results indicate that on average, the approximate policies can improve the total distance saving by more than 20% over various problem settings. Additionally, the results show that the performance of the PIA system is significantly influenced by the attractiveness of the parking incentive to drivers.

Household travelers typically need to coordinate their travel decisions in various aspects, such as destination and trip-timing, resulting in distinctive travel patterns compared to individual travelers. This paper investigates the morning commute problem in a “Y-shaped” network featuring “school near workplace” in consideration of both individual and household travelers and further understands the impact of school locations on the morning commute by comparing it with the “school near home” network. We analytically derive all equilibrium cases in the “school near workplace” network and classify them into three traffic patterns. We analyze the welfare effects of the staggering policy, finding that implementing the staggering policy in the “school near workplace” network can achieve a “win–win” scenario for individuals and household travelers in some equilibrium cases. Also, when the capacity ratio of school-constrained bottleneck to common-constrained bottleneck is below a threshold, which one is better (i.e., “school near workplace” or “school near home”) depends on the free-flow travel costs of the two networks. Locating the school “near workplace” is better than “near home” when the difference in the free-flow related costs for children between the two networks is not very large. Furthermore, we analyze the welfare effects of bottleneck expansion in the “school near workplace” network, finding that bottleneck expansion paradox may emerge when expanding the school-constrained bottleneck. Although properly designing the schedule gap can eliminate the paradox, a significant increase in the schedule gap may be needed to escape from the paradoxical cases when the common-constrained bottleneck capacity is slightly larger than the school-constrained bottleneck capacity. Our study sheds light on the importance of school locations in determining the performance of traffic management policies in the morning commute with both individual and household travelers.

Road geometry significantly influences the physical forces acting on vehicles and the perceptual ability of drivers. Unfortunately, most available car-following models ignore the influence of complex road geographical features, such as curvatures and slopes and thereby lack scalability. To fill these gaps, this study presents a framework for the construction of a geometry-aware car-following model. Under the over-alignment assumption, car-following motion on horizontal curves was simplified into seven internal or adjacent car-following scenarios. Two novel alternative vehicle control modes (centralized and decentralized) for car-following motions on a horizontal route were proposed. The structured features of each scenario, considering both lateral and longitudinal information, were defined mathematically. Open-source data with trajectory records and road surface conditions on highways in Japan were collected and used as empirical data sources. First, we analyzed the theoretical proportion of traffic scenarios that conformed to the traditional car-following model for any horizontal route. Several properties of the car-following scenario proportion were proposed and proved. Both empirical statistics and theoretical estimations showed the existence of real-world sizable car-following scenarios that could not be handled by traditional models. Owing to their powerful ability to handle complex input features, machining learning and deep learning models were applied in car-following behavior modeling to make multistep predictions. With high computational efficiency, the results were compared with those of models with traditional inputs to demonstrate the effectiveness of the proposed approach.