Large-scale Transportation Networks Research Articles

The Multi-Objective Shortest Path Problem with Multimodal Transportation for Emergency Logistics

The optimization of emergency logistical transportation is crucial for the timely dispatch of aid and support to affected areas. By incorporating practical constraints into emergency logistics, this study establishes a multi-objective shortest path mixed-integer programming model based on a multimodal transportation network. To solve multi-objective shortest path problems with multimodal transportation, we design an ideal point method and propose a procedure for constructing the complete Pareto frontier based on the k-shortest path multi-objective algorithm. We use modified Dijkstra and Floyd multimodal transportation shortest path algorithms to build a k-shortest path multi-objective algorithm. The effectiveness of the proposed multimodal transportation shortest path algorithm is verified using empirical experiments carried out on test sets of different scales and a comparison of the runtime using a commercial solver. The results show that the modified Dijkstra algorithm has a runtime that is 100 times faster on average than the modified Floyd algorithm, which highlights its greater applicability in large-scale multimodal transportation networks, demonstrating that the proposed method both has practical significance and can generate satisfactory solutions to the multi-objective shortest path problem with multimodal transportation in the context of emergency logistics.

Optimal allocation of autonomous PV-powered fast charging stations for electrified transportation considering traffic dynamics

In response to the imperative to reduce greenhouse gas emissions, global efforts are focusing on electrifying transportation and adopting renewable energy sources. This necessitates establishing reliable fast-charging stations (FCSs) for the widespread integration of electric vehicles (EVs). Indeed, the arbitrary distribution of FCSs can detrimentally affect the efficiency of traffic movement. Thus, this paper presents a planning methodology aimed at optimizing the allocation of FCSs within large-scale transportation networks. The energy system proposed includes an autonomous photovoltaic (PV)-powered FCS based on a DC microgrid, supplemented by a stationary battery energy storage system for backup power. The behavior of EVs within the traffic flow is modeled using a realistic traffic simulation software called Dynamic Network Assignment Simulation for Advanced Road Telematics (DYNASMAT). The conflicting objectives of minimizing total annual costs and EV average waiting time at stations are addressed through multi-objective optimization. To solve this optimization problem, the GAMS software is employed. A transportation network with 92 nodes and 284 links is utilized to test the proposed approach. The proposed autonomous PV-powered FCS is compared with Natural Gas-fired Distributed Generators (NGDG)-powered FCS. The simulation results show that while the NGDG-powered FCS exhibits higher emissions, the autonomous PV-powered equivalent operates emission-free. However, the annual cost of the autonomous PV-powered FCS is higher than that of the NGDG-powered FCS.

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 .

A Simulation-Based Method for Optimizing Remote Park-and-Ride Schemes

Urbanization places greater demand on the link between downtown areas and suburbs, due to commuters’ long-distance and diverse trips. As an emerging form of park-and-ride (PNR) services, remote PNR (RPR) facilities have proved to be more economical and environmentally friendly, allowing travelers to park in a suburban area and travel to a rail station via bus. In this regard, a generalized simulation-based bilevel model for optimizing the locations and capacities of RPR facilities is developed in this article. A hybrid algorithm integrating Bayesian optimization, branch and bound, and trust region sequential quadratic programming is proposed to achieve an optimal solution. The proposed integrated method balances the desired efficiency and accuracy through the combination of machine learning-based technology and mathematical optimization methodology. The validity of the proposed model is tested on a large-scale real-world transportation network in Halle, Germany. Modeling and analyzing RPR schemes using the proposed framework may provide new insights into improving social welfare.

European Funding for Sustainable Transport Systems—Influencing Factor of Regional Economic Development in Romania

Sustainable development is a core concept in regional development. Sustainability is characterized by supporting the building of resilient infrastructure and promoting the sustainable industry. In this context, sustainable transport is particularly important as it represents an opportunity for regional development. This research aims to quantify the impact of investments through structural instruments, specifically EU funds, on promoting a sustainable transport system and eliminating barriers from large-scale transport networks. This study focuses on the impact of these investments on regional economic development in Romania. The analysis used data from all eight development regions of the Romanian economy between 2014 and 2020. Panel data regression models, including the generalized difference method of moments (Dif-GMM) and the system GMM method (Sys GMM), were employed. This study confirms the idea that European structural and investment funds (ESIFs) play a positive role in promoting sustainable transport for regional economic development. Additionally, the quality of regional governance is identified as a key factor in economic development. This study, therefore, reveals a convergence effect between regions. Regions with a lower initial GDP per capita develop quicker compared to regions with a higher initial GDP per capita, indicating a “catch-up” effect. From a policy perspective, these issues can guide decision making and resource allocation.

Reliable Routing Strategies on Urban Transportation Networks

The problem of finding the most reliable routing strategy on urban transportation networks refers to determining the time-adaptive routing policy that maximizes the probability of on-time arrival at a destination given an arrival time threshold. The problem is defined on a stochastic and time-dependent network that captures real-world transportation systems’ inherent uncertainty and dynamism. To solve this problem, we present a dynamic programming–based algorithm that benefits from a node-time pairs queue implementation. In addition to improving the computational running time in most cases, this implementation supports different queue disciplines, leading to different algorithmic approaches: label-correcting and label-setting methods. We prove the correctness of the algorithm and derive its worst case time complexity. We present computational experiments over real-world, large-scale transportation networks with up to [Formula: see text] nodes, showing that the algorithm is a viable alternative to existing state-of-the-art methods. It can be four times faster for relatively tight arrival time thresholds and is competitive for looser ones. We also present experiments assessing the different queue disciplines used within the algorithm, the gains of the node–time pairs queue implementation, and comparing optimal strategies obtained from reliability and travel time objectives.

Efficient Vulnerability Assessment of Large-Scale Dynamic Transportation Networks

Efficient Vulnerability Assessment of Large-Scale Dynamic Transportation Networks

Estimating environmental impacts of large-scale transportation networks with vehicle registration data

Estimating environmental impacts of large-scale transportation networks with vehicle registration data

A customized two-stage parallel computing algorithm for solving the combined modal split and traffic assignment problem

Efficiently solving the traffic assignment problem (TAP) for large-scale transport networks is a critical problem for transportation studies. Most of the existing algorithms for TAP are serial ones based on single-computer mode, which has inherently limited the computational efficiency, compared with parallel computing methods. Thus, this paper aims to propose an efficient distributed multi-computer cluster resource allocation method for the parallel computing of TAP. Previous studies on the parallel computing of TAP are mainly based on a single-mode, which is extended to a more complex combined modal split and traffic assignment (CMSTA) case in this paper. In order to decompose the CMSTA problem, we proposed a block-decomposed model for solving the CMSTA problem. Then we designed an optimal parallel computing resource schedule for solving each block problem more quickly on the huge transportation network. Therefore, we implemented a customized two-stage parallel (TP) algorithm that can fully use parallel resources. The first parallel stage of the TP algorithm is used in the path generation phase, and the second parallel stage is used in the path flow adjustment phase. Besides, the parallel slowdown is uncovered in calculating each block problem of the path flow adjustment phase by using parallel resources. Numerical examples are taken to validate the efficiency and robustness of the proposed TP algorithm.

Customised bus route design with passenger-to-station assignment optimisation

As an emerging and innovative public transportation mode, the customised bus (CB) has drawn widespread attention. Existing studies of the CB mainly focus on issues regarding the bus station location, route design, timetabling, and fare setting, but rarely consider the passenger to bus station assignment problem which can significantly influence passengers’ benefit and the buses’ operating scheme. Thus, this study focuses on the customised bus routing problem with passenger-to-station assignment (CBRP-PSA) aiming to simultaneously minimise passengers’ CB service access cost and the bus route cost. A time-discretized multi-commodity network flow model is developed to jointly determine the CB routes, timetables, and passenger-to-station assignment by allowing split loads and mixed loads. Through the dualization, the developed model is decomposed into two solvable sub-problems, and a Lagrangian-based heuristic solution algorithm is proposed. The model and algorithm are implemented in illustrative, medium-scale, and large-scale transportation networks to demonstrate their effectiveness under different scenarios.

Real-Time Traffic Forecast System for the Accident-Prone Large-Scale Transportation Network in the Seoul Metropolitan Area

This study proposes a reliable and efficient real-time forecasting platform for use in an accident-prone large-scale transportation network. We showed the method could be applied to various roadway sections without any loss of performance or efficiency. Due to its robustness, efficiency, and versatility, the method could be implemented in the Seoul Metropolitan Area to provide traffic authorities and road users with future traffic information even under accident conditions. This is the major contribution of this research and contrasts with state-of-the-art techniques proposed by prior studies, which rely heavily on parameter tuning with large historical datasets and produce only site-specific forecasts with limited prediction horizons under recurrent traffic conditions. The proposed method makes no assumptions about the physical structure of the transportation network and can be applied to different roads under different traffic conditions without time constraints.

Optimal allocation of FCSs and PV units in smart grids considering traffic and generation dispatch

Worldwide, transportation electrification and photovoltaics (PVs) have become vital components of modern and future planning problems. Therefore, the large-scale deployment of electric vehicles (EVs) requires reliable fast-charging stations (FCSs). However, random allocation of FCSs and PV adversely impacts the performance and operation of the traffic flow and power networks. Thus, this paper proposes a new planning approach for the optimal allocation of FCSs and PVs in large-scale smart grids and transportation networks. Unlike existing approaches, the well-known Dynamic Network Assignment Simulation for Road Telematics (DYNASMART) is updated and employed in the proposed planning approach to accurately simulate the EVs within the traffic stream. The average waiting time of the EVs at the charging stations (including charging time) and the total annual costs are utilized as conflicting objective functions to be minimized. To effectively solve this comprehensive model with conflicting objectives, a bi-layer multiobjective optimization based on metaheuristic (NSGA-II) and deterministic (GAMS) algorithms is established. The outer layer optimizes the allocation of FCSs and PV accurately, while the inner one optimally dispatches the generated power of the conventional generation units. The proposed planning approach is tested on a 26-bus electrical transmission network and 92-node transportation system. The simulation results demonstrate the efficiency of the proposed approach. The optimal allocation of FCSs and PVs with 80% EVs penetration increases the net profit by 85.36% while maintaining a maximum of 30 minutes for both charging and waiting time.

Toward Large-Scale Graph-Based Traffic Forecasting: A Data-Driven Network Partitioning Approach

Network partitioning is recognized as an effective auxiliary approach for solving transportation tasks on large-scale traffic networks in a domain-decomposition (DD) manner. Most of the existing related partitioning algorithms are explicitly designed to traffic management problems and merely focus on the implied topology of the networks. In this article, toward the practical problems that happened to traffic forecasting (TF) tasks, we propose a network-partitioning-based DD framework to improve graph convolutional network (GCN)-based predictors’ performance on large-scale transportation networks. Particularly, we devise a data-driven network-partitioning approach, namely, speed-matching-partitioning (SMP), which employs not only the topological features but also the traffic speed observations of traffic networks for partitioning. Additionally, we propose a data-parallel training strategy that feeds partitioned subnetworks into independent predictors for parallel training. The proposed approach is tested by comprehensive case studies on three real-world data sets to evaluate its effectiveness. The results indicate that the proposed approach can help improve GCN-based predictors’ accuracy and training efficiency on both small and relatively large traffic data sets. Furthermore, we investigate the model sensitivity to the selection of graph representations and framework parameters, and the learning efficiency of the data-parallel training strategy.

Modeling Autonomous Vehicles Deployment in a Multilane AV Zone With Mixed Traffic

The advancement of technologies has fast-tracked the deployment of autonomous vehicles (AVs), however, they necessitate the modernization of the existing road infrastructure to meet the technical and safety requirements of AVs. Due to the high cost and prolonged time required for upgrading infrastructure, it is expected that AVs will be introduced gradually into the existing transport network of human-driven vehicles (HVs) either jointly or separately in a special area or zone within that network. In this paper, we propose a mathematical framework for modelling and deployment of multi-lane AV zone in a mixed-user traffic network that takes into account the different route choice decisions of HVs and AVs. The model is used to study two different designs for a multi-lane AV zone, where in Design 1, both lanes are dedicated for AVs inside the AV zone; while in Design 2, one lane is dedicated for AVs, and another lane is for the HV traffic. We then derive an equivalent convex formulation and develop a novel solution method for this complex problem, as well as evaluate and compare the performance of the designs in a realistic large-scale transportation network. Numerical results show that the deployment of a multi-lane AV zone improves network performance in terms of the total system travel time (TSTT). The proposed framework can be used to assist transportation planners and decision-makers to successfully deploy AVs seamlessly with mixed traffic.

Large-scale multimodal transportation network models and algorithms-Part II: Network capacity and network design problem

Transportation network capacity enhancement is essential in urban transportation planning. In this paper, a general multimodal network capacity problem (MNCP) is proposed, which can depict the transfers, mode overlap, and common line problem and congestion effect of transit. The problem is established as a bi-level model with combined mode choice and traffic assignment as the lower-level programming. Based on the MNCP, a tri-level multimodal network design problem (MNDP-MNCP) is developed to maximize the network capacity. The models are solved with an efficient Kriging-surrogate-based optimization algorithm in real-scale urban networks. Numerical results demonstrate the performances of the proposed framework.

Detecting Extreme Traffic Events Via a Context Augmented Graph Autoencoder

Accurate and timely detection of large events on urban transportation networks enables informed mobility management. This work tackles the problem of extreme event detection on large-scale transportation networks using origin-destination mobility data, which is now widely available. Such data is highly structured in time and space, but high dimensional and sparse. Current multivariate time series anomaly detection methods cannot fully address these challenges. To exploit the structure of mobility data, we formulate the event detection problem in a novel way, as detecting anomalies in a set of time-dependent directed weighted graphs. We further propose a Context augmented Graph Autoencoder (Con-GAE) model to solve the problem, which leverages graph embedding and context embedding techniques to capture the spatial and temporal patterns. Con-GAE adopts an autoencoder framework and detects anomalies via semi-supervised learning. The performance of the method is assessed on several city-scale travel-time datasets from Uber Movement, New York taxis, and Chicago taxis and compared to state-of-the-art approaches. The proposed Con-GAE can achieve an improvement in the area under the curve score as large as 0.15 over the second best method. We also discuss real-world traffic anomalies detected by Con-GAE.

GATC and DeepCut: Deep spatiotemporal feature extraction and clustering for large-scale transportation network partition

The network partition is an important method for many key transport problems, e.g., transport network zoning, parallel computing of traffic assignment problem, and analysis of the macroscopic fundamental diagram, to name a few. This paper designs two partition frameworks called GATC (Graph attention auto-encoder for clustering) and DeepCut, which can partition the transportation network into several components. These two frameworks combine unsupervised deep learning and clustering, taking into account both temporal factors and spatial factors. Firstly, the traffic flow time series data is encoded by graph attention auto-encoder, with graph structure and content considered. Secondly, the normalized cut method is used to partition the transportation network into several homogeneous sub-networks. DeepCut encodes the input data by a simple encoder, and the normalized cut method is used to partition the transportation network. The proposed methods are verified by a numerical example, which demonstrates the rationality and effectiveness of GATC and DeepCut for transportation network partition.

Large-scale multimodal transportation network models and algorithms-Part I: The combined mode split and traffic assignment problem

Modelling the combined mode split and traffic assignment (CMSTA) problem is essential to capture the travelers’ behaviors and predict the flow distribution in multimodal transportation networks. In this paper, a general fixed-point model that combines the CNL-based mode split and the VI-based traffic assignment is developed to formulate the CMSTA problem in a more realistic multimodal network. The transit common line problem and asymmetric travel cost are considered. A new solution algorithm that integrated with several effective methods and strategies is proposed to solve the fixed-point model in large-scale networks. Numerical results show the effectiveness of the proposed model and algorithm.

Physics-Based Use Case Analysis: Route Planning and Dynamic Load Prediction for Battery-Electric Buses

The electrification of transportation and goods delivery networks, including ground, marine, and air, is becoming a reality around the world. These networks are logistically complex, and with this transition there is an added knowledge gap in the understanding of the energy storage system, such as lithium-ion battery packs. In Seattle, the local transit agency King County Metro is a national leader in transitioning to a fully electrified bus fleet and is the case study for this work. As transit agencies, such as King County Metro, adopt electrified vehicles, there are many new parameters that must be considered in route planning, fleet maintenance, and charging logistics. The literature shows that a longitudinal vehicle dynamics model can be used to estimate energy and load requirements along a route [1,2]. This work utilizes these known vehicle models, vehicle specifications, and battery information in a software tool. Software inputs include geographical information system (GIS) data, ridership data, and vehicle acceleration profiles. Model results allow the user to rank routes using metrics designed to consider route efficiency, vehicle performance, and known battery degradation events, such as regenerative braking and high-power discharge. This work also introduces how the battery state of charge, overpotential, current window, and deviations from equilibrium influence the route rankings. These results provide both systems-level and module-level insights beyond conventional vehicle and logistics models, allowing the user to make decisions that are predictive rather than reactive and can improve the lifetime of the batteries on-board.[1] Gallet, M., Massier, T. & Hamacher, T. Estimation of the energy demand of electric busesbased on real-world data for large-scale public transport networks. Appl. Energy 230,344–356 (2018).[2] Asamer, J., Graser, A., Heilmann, B. & Ruthmair, M. Sensitivity analysis for energydemand estimation of electric vehicles. Transp. Res. Part D Transp. Environ. 46, 182–199(2016).

Transit Voronoi diagrams in multi-mode public transport networks

Network Voronoi diagrams (N-VDs) are effective geometric constructions for partitioning geographical space constrained by road networks. However, they are not applicable for urban areas with well-developed public transport systems. This study proposes new transit Voronoi diagram (T-VD) models for partitioning geographical space constrained by public transport networks. The proposed T-VD models explicitly consider the complexities of a public transport network, including transfers between different transport modes, dynamic transit schedules, changing network topologies, etc. To efficiently construct T-VDs, geo-computational algorithms are developed by modifying and integrating classical shortest-path algorithms in road networks and transit shortest-path algorithms in public transport networks. Case study results show significant differences between T-VDs and N-VDs, highlighting the need for using T-VDs in urban areas with well-developed public transport systems. The developed geo-computational algorithms efficiently constructed T-VDs in large-scale public transport networks within satisfactory computational times.