Transportation Network Problems Research Articles

A Study of Distribution Problems Based on Express Transportation Networks

This article uses the distribution data of rookie stations in Jinan City to explore the allocation problem of express delivery transportation networks. Firstly, in response to the uneven spatial layout of rookie Station, a corresponding evaluation index system was established based on K-means and TOPSIS models. The results showed that rookie Station exhibited characteristics of agglomeration in the central urban area and dispersion in the remote urban area. Secondly, by establishing a Floyd model to plan the route, the most reasonable transfer center was determined, and the results showed that the Shizhong District was the most suitable transfer station for Jinan City, while Zhangqiu District was selected as the second transfer station. Finally, based on the population density and express delivery demand in Jinan City, a model for express delivery allocation business points was established, and the optimal solutions for express delivery allocation business points in each district were obtained.

A Sampling Strategy for High-Dimensional, Simulation-Based Transportation Optimization Problems

When tackling high-dimensional, continuous simulation-based optimization (SO) problems, it is important to balance exploration and exploitation. Most past SO research focuses on the enhancement of exploitation techniques. The exploration technique of an SO algorithm is often defined as a general-purpose sampling distribution, such as the uniform distribution, which is inefficient at searching high-dimensional spaces. This work is motivated by the formulation of exploration techniques that are suitable for large-scale transportation network problems and high-dimensional optimization problems. We formulate a sampling mechanism that combines inverse cumulative distribution function sampling with problem-specific structural information of the underlying transportation problem. The proposed sampling distribution assigns greater sampling probability to points with better expected performance as defined by an analytical network model. Validation experiments on a toy network illustrate that the proposed sampling distribution has important commonalities with the underlying and typically unknown true sampling distribution of the simulator. We study a high-dimensional traffic signal control case study of Midtown Manhattan in New York City. The results show that the use of the proposed sampling mechanism as part of an SO framework can help to efficiently identify solutions with good performance. Using the analytical information for exploration, regardless of whether it is used for exploitation, outperforms benchmarks that do not use it, including standard Bayesian optimization. Using the analytical information for exploration only yields solutions with similar performance than when the information is used for exploitation only, reducing the total compute times by 65%. This paper sheds light on the importance of developing suitable exploration techniques to enhance both the scalability and the compute efficiency of general-purpose SO algorithms. Funding: T. Tay thanks the Agency for Science, Technology and Research (A*STAR) Singapore for funding his work. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0110 .

Multimodal network path optimization based on a two‐stage algorithm in the perspective of sustainable transport development

AbstractThe environmental issues brought on by carbon emissions from transport have risen to prominence in recent years. More and more academics are using the multi‐objective path optimization method to solve the multimodal optimization problem from the standpoint of sustainable development in order to address the environmental issues brought on by the transport process. The research proposes a two‐stage method to handle multi‐objective optimization convergence and simplify multimodal transport path optimization. In the first stage, a fuzzy C clustering model is established, and based on the clustering results, the multimodal transport network nodes are identified. In the second stage, a multimodal transport multi‐objective path optimization model is established, and the optimal path is solved using a genetic algorithm. The research method was applied in the Bohai Rim region. Results indicated that the fuzzy C‐clustering method and the genetic method were able to select the optimal node city, thus solving the actual site selection problem of multimodal transportation networks. Using the FCM model, the 86 city nodes were categorized into four types, leading to the establishment of the most proficient multimodal transportation network in the Bohai Rim region. Using a genetic algorithm for optimization, a stable state is reached after 25 iterations. In the validation experiment on path optimization, the cost was reduced by 47.12% compared to the minimum single objective time, and transportation carbon emissions saw a reduction of 28.23%. Similarly, compared to the lowest target for transportation carbon emissions, the cost was reduced by 39.48% and the time was reduced by 38.12%. Compared to the lowest target for transportation carbon emissions, the time was reduced by 32.02% and the carbon emissions were reduced by 19.23%. Notably, the transportation multi‐objective path optimization model showed significant improvement compared to the single‐target model. The research method has been proven to be superior, and can offer the most optimal transportation route guidance for participants in multimodal transportation. Furthermore, it can effectively tackle the issue of node selection convergence and multi‐objective optimization, while also serving as a valuable source of data to support the theoretical advancement of multimodal transportation network path optimization.

Traveling itinerary problem in a scheduled multimodal transportation network for a fixed sequence of cities.

Developing an efficient and economical journey plan in multimodal transportation networks is of significant and fast-growing importance, but it is still an annoying experience for a traveler. This paper aims to find the journey plan at a combined cross-border and inter-regional level when visiting a sequence of cities while utilizing several transport modes to reduce travel costs and planning time. We study a traveling itinerary problem in a scheduled multimodal transportation network with constraints on both arcs and nodes as a new extension of the shortest path problem. We formulate a 0-1 integer linear programming model for the traveling itinerary problem and develop an exact algorithm that finds a combined cross-border and inter-regional low costs journey plan. We present case studies based on real-world transport data to illustrate the usefulness and computational efficiency of the proposed approaches. We compare the results with the previously proposed approach to demonstrate the benefits of multimodal journeys. Finally, we compare the results with the solution obtained by the general-purpose 0-1 integer linear programming solver to evaluate the computational time.

GE-DDRL: Graph Embedding and Deep Distributional Reinforcement Learning for Reliable Shortest Path: A Universal and Scale Free Solution

This paper studies the reliable shortest path (RSP) problem in stochastic transportation networks. State-of-the-art RSP solutions usually target one specific RSP problem; moreover, the corresponding algorithm’s computational complexity scales at least linearly with the size of the underlying transportation network. While in this paper, we propose a graph embedding and deep distributional reinforcement learning (GE-DDRL) method, which serves as a universal and scale-free solution to the RSP problem. GE-DDRL uses deep distributional reinforcement learning (DDRL) to estimate the full travel-time distribution of a given routing policy, and improves the given routing policy with the generalized policy iteration (GPI) scheme. Further, in order to achieve the generalization ability to new destination nodes, we employ one of the canonical graph embedding techniques (Skip-Gram) to compress the nodes’ representation into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$</tex-math> </inline-formula> -dimensional real-valued vectors. With the properly compressed node features, GE-DDRL is able to generalize its estimation of the routing policy’s travel-time distribution to untrained destination nodes, and hence achieve the ‘all-to-all’ navigation functionality. To the best of our knowledge, GE-DDRL serves as the first RSP planner, which applies simultaneously to almost all RSP objectives and in the meanwhile, is scale free with the size of the transportation network in terms of the online decision-making time and memory complexity. Experimental results and comparisons with state of the arts show the efficacy and efficiency of GE-DDRL in a range of transportation networks.

Optimization of fleet size and determining the optimal allocation of wagons in minerals transport network: A case study of Iran’s railway system

Rail wagon planning is an important type of planning in the rail transport network since its costs are high. Therefore, enhancing the accuracy of fleet planning could result in cost efficiency. In this research, we aim to model and optimize the fleet size and allocation problem in a rail transportation network. We proposed a novel two-step algorithm composed of two meta-heuristic steps to optimize the allocation of the fleet between origins and destinations and to determine the optimal number of wagons. Besides, we provided a case study of Iran’s minerals rail transport network. The results demonstrate that 4925 wagons are required to transport approximately 17.7 million tons of minerals per year, roughly 10% less than the current proportion. The validity of the results was also analyzed by using a comparison between our algorithm and CEPLEX for a test dataset. It is shown that our approach outperforms CEPLEX in terms of speed while retaining the same performance.

Spatio-Temporal Characterization of Stochastic Dynamic Transportation Networks

This paper addresses the problem of modeling stochastic dynamic transportation networks, where travel times are random variables with time-varying distributions and spatio-temporal dependencies. It presents a taxonomy of modeling approaches used in the literature and defines extended taxonomic categories to better capture the characteristics of transportation networks. A two-part approach is presented to characterize transportation networks spatially and temporally based on the network structure and observed travel time data. The first part implements a community detection approach from network science modified for the application to transportation networks. The community detection approach is further adapted to use dynamic time warping distance to measure the dissimilarity between time series of link travel times. The second part implements a change point detection approach that identifies points in time when there are changes in the underlying link travel time distributions generating the observed time series. The proposed approach is tested via sensitivity analysis using the large-scale Chicago network and its corresponding travel time data. Sensitivity analysis with varying model parameters demonstrates the robustness of the network characterization and helps determine the most suitable parameter values. The sensitivity analysis with respect to varying weather conditions and demand patterns in the network demonstrates that the spatial and temporal characteristics are robust with respect to changing exogenous and endogenous conditions.

DRL Router: Distributional Reinforcement Learning-Based Router for Reliable Shortest Path Problems

This article studies reliable shortest path (RSP) problems in stochastic transportation networks. The term reliability in the RSP literature has many definitions, e.g., 1) maximal stochastic on-time arrival probability, 2) minimal travel time with a high-confidence constraint, 3) minimal mean and standard deviation combination, and 4) minimal expected disutility. To the best of our knowledge, almost all state-of-the-art RSP solutions are designed to target one specific RSP objective, and it is very difficult, if not impossible, to adapt them to other RSP objectives. To bridge the gap, this article develops a distributional reinforcement learning (DRL)-based algorithm, namely, DRL-Router, which serves as a universal solution to the four aforementioned RSP problems. DRL-Router employs the DRL method to approximate the full travel time distribution of a given routing policy and then makes improvements with respect to the user-defined RSP objective through a generalized policy iteration scheme. DRL-Router is 1) universal, i.e., it is applicable to a variety of RSP objectives; 2) model free, i.e., it does not rely on well calibrated travel time distribution models; 3) it is adaptive with navigation objective changes; and 4) fast, i.e., it performs real-time decision making. Extensive experimental results and comparisons with baseline algorithms in various transportation networks justify both the accuracy and efficiency of DRL-Router.

Traffic assignment in urban transportation network problem with emission constraints in China: a cooperative game theory

Traffic assignment in urban transport planning is the process of allocating traffic flows in a network. Traditionally, traffic assignment can reduce travel time or travel costs. As the number of vehicles increases and congestion causes increased emissions, environmental issues in transportation are gaining more and more attention. The main objective of this study is to address the issue of traffic assignment in urban transport networks under an abatement rate constraint. A traffic assignment model based on cooperative game theory is proposed. The influence of vehicle emissions is incorporated into the model. The framework consists of two parts. First, the performance model predicts travel time based on the Wardrop traffic equilibrium principle, which reflects the system travel time. No travelers can experience a lower travel time by unilaterally changing their path. Second, the cooperative game model gives link importance ranking based on the Shapley value, which measures the average marginal utility contribution of links of the network to all possible link coalitions that include the link, and assigns traffic flow based on the average marginal utility contribution of a link with system vehicle emission reduction constraints. The proposed model shows that traffic assignment with emission reduction constraints allows more vehicles in the network with an emission reduction rate of 20% than traditional models.

A Joint Trajectory Planning and Signal Control Framework for a Network of Connected and Autonomous Vehicles

The advancement of the Intelligent Transport Systems (ITS) and the emerging Connected and Automated Vehicles (CAVs) technology are acknowledged to hold a great potential to mitigate challenging problems in the current transportation networks. Particularly, a proper traffic control strategy with a precise vehicular movement control scheme can alleviate the congestion and improve the safety and efficiency of the traffic. This paper proposes a novel bi-level control framework that combines a design of traffic signal timings at a network level, and a detailed trajectory control policy for individual vehicles at a link-level within a network of CAVs. We develop a group-based longitudinal trajectory planning scheme to coordinate vehicular movements at the lower level of our framework while abiding by the signal operations along with end-to-end vehicle routing decisions from the upper network level optimization. This joint and mutual interaction between the two different control strategies in the urban signalized corridors is complex and can significantly affect the overall network’s performance, nevertheless has not been explored previously in the literature. The proposed framework enables such studies where we derive an efficient algorithm that iteratively solves the mixed-integer linear programming (MILP) and linear programming models in each link at the lower and over the network at the upper levels, respectively. Numerical results show the effectiveness of the proposed joint control framework in network performance regarding the average travel time, queue formation and dissipation across the network.

Exploring the Optimization of Synchromodal Transportation Path under Uncertainties

Along with the rapid development of the economy and the increasing demand for transport quality, renewed attention to multimodal transportation has emerged. However, due to the unpredictable transport environment in the process of multimodal transportation and the uncertainties caused by the change of transport market demand, transport decision-makers face many difficulties in transport planning and routing, which has become an obstacle to the development of multimodal transportation. As an advanced form of multimodal transportation, synchromodal transportation has received extensive attention in recent years. Due to its flexibility and sustainability, synchromodal transportation can effectively deal with the uncertainty in multimodal transportation. Based on the problem of multimodal transportation networks with uncertainties, this paper proposed a mixed time-window-constrained path optimization model with the goal of minimizing the total transportation costs, and proposed corresponding assumptions considering three types of uncertainty. The model could be solved by a genetic algorithm using MATLAB software. Using this model, the best transportation path and the optimal scheme considering synchronization were obtained. The results of the case study showed that synchromodal transportation can adjust the transportation plan in time to respond to uncertainties, thus, effectively reducing transportation costs. This paper favorably supported the introduction of synchromodal transportation, which is of significance to the development of multimodal transportation in the future.

Regularity Index of Uncertain Random Graph

A graph containing some edges with probability measures and other edges with uncertain measures is referred to as an uncertain random graph. Numerous real-world problems in social networks and transportation networks can be boiled down to optimization problems in uncertain random graphs. Actually, information in optimization problems in uncertain random graphs is always asymmetric. Regularization is a common optimization problem in graph theory, and the regularity index is a fundamentally measurable indicator of graphs. Therefore, this paper investigates the regularity index of an uncertain random graph within the framework of chance theory and information asymmetry theory. The concepts of k-regularity index and regularity index of the uncertain random graph are first presented on the basis of the chance theory. Then, in order to compute the k-regularity index and the regularity index of the uncertain random graph, a simple and straightforward calculating approach is presented and discussed. Furthermore, we discuss the relationship between the regularity index and the k-regularity index of the uncertain random graph. Additionally, an adjacency matrix-based algorithm that can compute the k-regularity index of the uncertain random graph is provided. Some specific examples are given to illustrate the proposed method and algorithm. Finally, we conclude by highlighting some potential applications of uncertain random graphs in social networks and transportation networks, as well as the future vision of its combination with symmetry.

Multimodal transportation network with cargo containerization technology: Advantages and challenges

Traditional multimodal transportation dispatching of roads, railways, and maritime has been studied for years. This study investigates a bulk cargo distribution problem in a multimodal transportation network considering both the transportation modes of inland waterways and containerization technology. First, we qualitatively discuss the advantages and challenges of transportation using cargo containerization technology from the aspects of transportation cost, time-saving, pollutant emission reduction, and customer preference. Second, a mixed-integer linear programming model is developed, wherein the influence of bridge heights on transportation is considered. The model is validated based on near-practice numerical experiments, and we can obtain the optimal transportation scenario by solving the model, including transportation mode selection, vehicle routing, depot selection, and cargo quantity for containerization. Third, we conduct numerical experiments based on the multimodal transportation network of the Yangtze River in China to discuss the performance of multimodal transportation with containerization technology considerations. The results show that containerization transportation can decrease the total transportation costs for logistics companies under certain parameter configurations. The model and experiments are then extended by considering pollutant emission reductions and time savings, respectively. We uncover the performance of containerization technology by analyzing the Pareto fronts of this technology and traditional bulk cargo transportation. This study elucidates the decision making of logistics companies and government policymaking.

Navigation With Time Limits in Transportation Networks: A Fourth Moment Approach

This paper investigates the stochastic on-time arrival (SOTA) problem in transportation networks. We propose a fourth moment approach (FMA), which calculates the tight lower bound of a given routing policy’s on-time-arrival probability, through estimating the first four moments of the policy’s travel time. Then, we employ the generalized policy iteration (GPI) scheme to gradually improve the policy towards the optimal one. Different from state-of-the-art algorithms for the SOTA problem, which require the full travel time distribution and usually incur high computational cost due to the convolution integration operation, FMA only requires the moments of travel-time statistics, which are easily estimated from the statistics perspective. Moreover, the algorithm’s computational complexity analysis indicates the relatively light computational load requirement of FMA. Experimental results in a range of transportation networks show FMA’s superior performance over state of the arts.

Megaregional Passenger Transportation Hub Location Problem Considering Congestion Effects

The need to make effective plans for locating transportation hubs is of increasing importance in the megaregional area, as recent research suggests that the growing intercity travel demand affects the efficiency of a megaregional transportation system. This paper investigates a hierarchical facility location problem in a megaregional passenger transportation network. The aim of the study is to determine the locations of hub facilities at different hierarchical levels and distribute the demands to these facilities with minimum total cost, including investment, transportation, and congestion costs. The problem is formulated as a mixed-integer nonlinear programming model considering the service availability structure and hub congestion effects. A case study is designed to demonstrate the effectiveness of the proposed model in the Wuhan metropolitan area. The results show that the congestion effects can be addressed by reallocating the demand to balance the hub utilisation or constructing new hubs to increase the network capacity. The methods of appropriately locating hubs and distributing traffic flows are proposed to optimise the megaregional passenger transportation networks, which has important implications for decision makers.

A Bicriteria Approach for Saving a Path Maximizing Dynamic Contraflow

The maximum dynamic contraflow problem in transportation networks seeks to maximize the flow from a source to a sink within a given time horizon with a possibility of arc reversals. This may result into blockage of paths of desired length from some node of the network towards the source. In some cases such as the evacuation planning, we may require a path towards the source to move some facilities, for example, emergency vehicles. In this work, we model the problem of saving such a path as a bicriteria optimization problem which minimizes the length of the path and maximizes the dynamic flow with arc reversals. We use the [Formula: see text]-constraint approach to solve the problem and propose a procedure that gives the set of all Pareto optimal solutions in a single-source-single-sink network with integer inputs. We also present computational performance of the algorithm on a road network of Kathmandu city, and on randomly generated networks. The results are of both theoretical and practical importance.

Electric Vehicle Charging Facility Planning Based on Flow Demand—A Case Study

The location of electric vehicle charging facilities is of great significance in promoting the use of electric vehicles. Most existing electric vehicle location models, including the flow refueling location model (FRLM) and its flexible reformulation (FRFRLM), are based on flow demand. At present, these models cannot effectively deal with large-scale traffic networks within a limited time, and there has been little comparison of their relative benefits and limitations. Additionally, there have been few evaluations of the actual construction and location of charging facilities in cities. This paper describes an algorithm that can solve the large-scale transportation network problem within a reasonable time. Using this algorithm, the FRLM and FRFRLM models are compared in a case study focused on Jiading District, Shanghai, China, which provides some direction for the future development of flow demand models. Finally, to evaluate the actual construction of urban charging facilities, this paper presents an algorithm that can map the actual charging facilities to the transportation network, and compares the actual construction situation with the model output. This enables a comprehensive evaluation of the actual construction of charging facilities and provides guidance for future construction.

Emergency response facility location in transportation networks: A literature review

Emergency response activity relies on transportation networks. Emergency facility location interacts with transportation networks clearly. This review is aimed to provide a combined framework for emergency facility location in transportation networks. The article reveals emergency response activities research clusters, issues, and objectives according to keywords co-occurrence analysis. Four classes of spatial separation models in transportation networks, including distance, routing, accessibility, and travel time are introduced. The stochastic and time-dependent characteristics of travel time are described. Travel time estimation and prediction method, travel time under emergency vehicle preemption, transportation network equilibrium method, and travel time in degradable networks are demonstrated. The emergency facilities location models interact with transportation networks, involving location-routing model, location models embedded with accessibility, location models embedded with travel time, and location models employing mathematical program with equilibrium constraints are reviewed. We then point out the-state-of-art challenges: ilities-oriented, evolution landscape and sequential decision modelling, data-driven optimization approach, and machine learning-based algorithms. • Emergency response facility location problem in transportation networks is reviewed for taxonomy purpose. • Travel time estimation and forecast methods in various scenarios are summarized. • Emergency facility location models in transportation networks are summarized and categorized. • Ilities-oriented, sequential decision modeling, data-driven optimization, and machine learning algorithm are to be studied.

Heuristic methods for minimal controller location set problem in transportation networks

To be used efficiently, transportation networks require control strategies to optimize flow distribution. However, the location and type of controllers employed in a transportation network directly impact the level of performance reachable by traffic control policies. In order to guarantee that the highest level of performance is reachable, fully exploiting the available network capacity and reducing negative externalities, we explore methods to identify relevant controller locations in transportation networks while minimizing the number of controllers used. Previous work provided an exact approach to identify controller locations on complex networks. However, said approach exhibits complications when applied to transportation networks containing bi-directional links. We therefore propose simple heuristic algorithms relying only on topological information to solve this problem, while also avoiding heavy computation, thus being able to determine a solution to the minimal controller location set problem on large networks. Based on the existing framework, we aim to provide an experimental setup, with diverse network sizes and configurations to analyze the performances of different heuristic methods, in order to develop an efficient algorithm.

A General Framework for Intersection Traffic Control With Backpressure Routing

Traffic congestion has long been a worldwide difficult problem in metropolitan transportation networks, incurring tremendous time waste and exhaust pollution. Extensive efforts have been made to address this problem, among which traffic control at intersections is known to be especially crucial while challenging. Also, it is helpful to the recent advent of autopilot technologies by enhancing the schedule accuracy and reducing the infrastructure cost. In practice, however, existing researches can hardly work well in a pervasive manner since they are essentially limited to two ideal assumptions: 1) each intersection comprises four ways; 2) each way is homogeneously composed of two lanes. Through an in-depth examination of their basic models, we find that the two-fold ideal assumptions are largely compelled by the surprisingly high complexity of converting a practical intersection topology into a theoretical conflict graph. Moreover, existing works seldom consider the modeling of complex intersections in the scene of cooperative control of multiply intersections. Driven by the above understandings, our first effort towards a general framework (for handling multiple-way heterogeneous intersections) is to carefully transform a practical intersection topology into a homomorphic, regular conflict graph which is suited to theoretical modeling and further processing with an affordable complexity. Besides, a maximum weight independent set (MWIS) based approach is proposed to minimize the average waiting time of vehicles at an isolated intersection. In addition, we apply a backpressure-based algorithm to our framework to further optimize the global average waiting time of vehicles in a whole road network. Simulation results have demonstrated that the average waiting time achieved by our approach is merely 80 seconds while traditional traffic light control reaches 370 seconds.