Urban Transportation Network Research Articles

Mean Field Multi-Agent Reinforcement Learning Method for Area Traffic Signal Control

Reinforcement learning is an effective method for adaptive traffic signal control in urban transportation networks. As the number of training rounds increases, the optimal control strategy is learned, and the learning capabilities of deep neural networks are further enhanced, thereby avoiding the limitations of traditional signal control methods. However, when faced with the sequential decision tasks of regional signal control, it encounters issues such as the curse of dimensionality and environmental non-stationarity. To address the limitations of traditional reinforcement learning algorithms applied to multiple intersections, the mean field theory is applied. This models the traffic signal control problem at multiple intersections within a region as interactions between individual intersections and the average effects of neighboring intersections. By decomposing the Q-function through bilateral estimation between the agent and its neighbors, this method reduces the complexity of interactions between agents while preserving global interactions between the agents. A traffic signal control model based on Mean Field Multi-Agent Reinforcement Learning (MFMARL) was constructed, containing two algorithms: Mean Field Q-Network Area Traffic Signal Control (MFQ-ATSC) and Mean Field Actor-Critic Network Area Traffic Signal Control (MFAC-ATSC). The model was validated using the SUMO simulation platform. The experimental results indicate that across different metrics, such as average speed, the mean field reinforcement learning method outperforms classical signal control methods and several existing approaches.

AI-Powered Intelligent Seaport Mobility: Enhancing Container Drayage Efficiency through Computer Vision and Deep Learning

The rapid urbanization phenomenon has introduced multifaceted challenges across various domains, including housing, transportation, education, health, and the economy. This necessitates a significant transformation of seaport operations in order to optimize smart mobility and facilitate the evolution of intelligent cities. This conceptual paper presents a novel mathematical framework rooted in deep learning techniques. Our innovative model accurately identifies parking spaces and lanes in seaport environments based on crane positions, utilizing live Closed-Circuit Television (CCTV) camera data for real-time monitoring and efficient parking space allocation. Through a comprehensive literature review, we explore the advantages of merging artificial intelligence (AI) and computer vision (CV) technologies in parking facility management. Our framework focuses on enhancing container drayage efficiency within seaports, emphasizing improved traffic management, optimizing parking space allocation, and streamlining container movement. The insights from our study provide a foundation that could have potential implications for real-world applications. By integrating cutting-edge technologies, our proposed framework not only enhances the efficiency of seaport operations, but also lays the foundation for sustainable and intelligent seaport systems. It signifies a significant leap toward the realization of intelligent seaport operations, contributing profoundly to the advancement of urban logistics and transportation networks. Future research endeavors will concentrate on the practical implementation and validation of this pioneering mathematical framework in real-world seaport environments. Additionally, our work emphasizes the crucial need to explore further applications of AI and CV technologies in seaport logistics, adapting the framework to address the evolving urbanization and transportation challenges. These efforts will foster continuous advancements in the field, shaping the future of intelligent seaport operations.

A traffic-fractal-element-based congestion model considering the uneven distribution of road traffic

The uneven distribution of road traffic has been proven to play a crucial role in congestion evolution within urban transportation networks. Establishing congestion models to explore congestion evolution under its impact is of great significance for predicting congestion. Current congestion models commonly describe the uneven distribution of traffic on roads through continuous differential equations or the simulation of vehicle operational rules by which to model traffic flow dynamics. However, these models fail to directly reveal the mechanism by which the uneven distribution characteristics affect congestion, which is not conducive to uncovering the fundamental factors that affect congestion evolution and general laws for predicting congestion. Therefore, this paper first constructs traffic fractal elements (TFE) based on the fractal characteristics exhibited by uneven traffic distribution. A local parameter namely the load duty ratio (LDR) is introduced to represent the uneven distribution level of TFE. By employing the fractal interpolation function (FIF), we model road traffic as the iterations of TFE. Then we define the fractal evolution rule and the load transfer rule to inscribe the influence relationship between LDR of TFE and global congestion, ultimately establishing a TFE-based congestion model. Furthermore, the proposed TFE-based model is validated using empirical data from AMAP and simulation data from a calibrated Cellular Automata (CA) model. Finally, by comparing the results with a CA-based congestion model, the LDR of TFE is confirmed to be a comprehensive and indicative parameter affecting congestion evolution. By simulating and fitting the congestion evolution data under different LDRs, our proposed model demonstrates its value in congestion prediction and analysis.

An MILP Method for Optimal Planning of Electric Vehicle Charging Stations in Coordinated Urban Power and Transportation Networks

The growing urban mobility and the proliferation of electric vehicles (EVs) have called for expansions of electric vehicle charging stations (EVCSs) as well as large upgrades to power distribution networks (PDNs) and transportation networks (TNs). This paper proposes an MILP method for the coordinated planning of coupled PDN-TN (CPTN), which determines the optimal deployment of new roads in TN, optimal placement of EVCSs along those roads, and optimal expansion of PDNs to accommodate EVCSs. First, we analyze drivers' charging and routing behaviors, then develop and solve a bi-level discrete TN model with EVs. Next, we formulate a bi-level CPTN model which includes CPTN investment and PDN operation decisions at the upper level and traffic flow assignment at the lower level. Third, we improve the traffic assignment model to better capture its interplay with EVCS and road investments. Moreover, to tackle the computational complexity of the proposed bi-level model and potential infeasibility under existing method, we propose an improved Big-M method to acquire a tractable MILP solution. Finally, we present numerical studies for a modified real-world urban TN, which verify the effectiveness of the proposed CPTN method. In the proposed case studies, the necessity and potential benefits of the coordination between TN and PDN and the consideration of user's rationality are investigated.

Practical Strategies for Urban Rail Transit Loop Planning, Construction, and Operation

Urban rail transit loops are essential in urban rail transit systems and transportation networks. However, precise requirements and reference standards for rail transit loop design have yet to be established. There are certain areas for improvement in planning, construction, and operation. In the planning and design of urban rail transit loops, the scale of the city and the relationship between line operations should be considered to ensure that the line conforms to the city’s operating traffic conditions and can effectively cater to peak passenger flow requirements. This article presents strategies for planning, constructing, and operating urban rail transit loops, laying the foundation for the healthy operation of urban rail transit.

Resilience Assessment Framework for an Urban Road Network Subjected to Disruptions

This research paper introduces a novel framework for assessing the resilience of urban transport networks. Traditional approaches to quantify resilience primarily focus on evaluating resilience based on the accessibility and vulnerability of network links. However, this study proposes a different approach by considering the dynamic role of links within the overall network, including their ability to accommodate additional traffic demand from other links. To accomplish this, a comprehensive scanning network approach is implemented using an iterative traffic assignment performed. The framework is applied to the urban transport network of Karachi, Pakistan. The model incorporates parameters for calibrations derived from volume delay functions, specifically alpha and beta, which are obtained from actual traffic travel times during free-flow and peak hours. The accuracy of the model is validated by comparing observed traffic counts at various locations with the simulated traffic volumes, resulting in an average error of 9% and a standard deviation of 7.4%. The study's findings reveal significant losses within the network and identify critical links that are particularly susceptible to disruptions. The analysis provides valuable insights into delays experienced by travellers and the additional distances travelled when specific links are closed. Furthermore, the framework enables the evaluation of the overall impacts of disruptions at the network level. The proposed methodology will allow traffic engineers and decision-makers to effectively plan rerouted through alternative links during disruptions.

Optimizing the geometry of transportation networks in the presence of congestion.

Urban transport systems are gaining in importance, as an increasing share of the global population lives in cities and mobility-based carbon emissions must be reduced to mitigate climate change and improve air quality and citizens' health. As a result, public transport systems are prone to congestion, raising the question of how to optimize them to cope with this challenge. In this paper, we analyze the optimal design of urban transport networks to minimize the average travel time in monocentric as well as in polycentric cities. We suggest an elementary model for congestion and introduce a numerical method to determine the optimal shape among a set of predefined geometries considering different models for the behavior of individual travelers. We map out the optimal shape of fundamental network geometries with a focus on the impact of congestion.

Reliable route planning and time savings in real-world urban intermodal transportation networks: Evidence from Hamburg, Germany

Reliable route planning and time savings in real-world urban intermodal transportation networks: Evidence from Hamburg, Germany

A non-additive path-based reward credit scheme for traffic congestion management

This study investigates the potential of non-additive path-based pricing for congestion management in urban transportation networks. We propose a novel path-based reward credit scheme to provide commuter incentives with the goal of reducing traffic congestion. We consider that a known proportion of commuters subscribe to this reward credit scheme and may earn credits when traveling in the network. We introduce a bilevel optimization formulation to determine optimal non-additive, path-based reward credits under traffic equilibrium conditions. In this formulation, the follower problem is a parameterized user equilibrium traffic assignment problem with two classes of users and non-additive path costs. We develop a single-level reformulation based on its first-order optimality conditions and derive theoretical properties of the reward credit scheme. Customized branch-and-bound algorithms are designed to solve the problem. We also introduce a heuristic approach that repeatedly solves parameterized follower problems to enhance scalability. We report numerical results that demonstrate the computational efficiency of the proposed methods over a benchmarking approach. We conduct a comprehensive evaluation of this path-based reward credit scheme compared with a link-based subsidy pricing scheme. We find that, on average, under a limited budget and a user participation level of at least 40%, the proposed path-based incentive mechanism yields larger reductions in traffic congestion over link-based approaches.

Urban Traffic Flow Congestion Prediction Based on a Data-Driven Model

Intelligent transportation systems need to realize accurate traffic congestion prediction. The spatio-temporal features of traffic flow are essential to analyze and predict congestion. Our study proposes a data-driven model to predict the traffic congested flow. Firstly, the traffic zone/grid method is used to store the local area roads’ average speed of the vehicles. Second, the discrete snapshot set is proposed to characterize traffic flow’s spatial and temporal features over a continuous period. Third, the evolution of traffic congested flow in various time dimensions (weekly days, weekend days, and one week) is examined by transforming the global urban transportation network into traffic zones. Finally, the data-driven model is constructed to predict urban road traffic congestion by using the extracted spatio-temporal characteristics of traffic zones’ traffic flow, the snapshot set of which serves as inputs for this model. The model adopts the convolutional LSTM network to learn the temporal and local spatial features of traffic flow, while utilizing a convolutional neural network to effectively capture the global spatial features inherent in traffic flow. The numerical experiments are conducted on two cities’ transportation networks, and the results demonstrate that the performance of the proposed model outperforms traditional traffic flow prediction models.

Value creation management in urban transportation networks based on soft systems methodology

Value creation management in urban transportation networks based on soft systems methodology

A hybrid adaptive large neighborhood search for time-dependent open electric vehicle routing problem with hybrid energy replenishment strategies.

As competition intensifies, an increasing number of companies opt to outsource their package distribution operations to professional Third-Party Logistics (3PL) fleets. In response to the growing concern over urban pollution, 3PL fleets have begun to deploy Electric Vehicles (EVs) to perform transportation tasks. This paper aims to address the Time-Dependent Open Electric Vehicle Routing Problem with Hybrid Energy Replenishment Strategies (TDOEVRP-HERS) in the context of urban distribution. The study considers the effect of dynamic urban transport networks on EV energy drain and develops an approach for estimating energy consumption. Meanwhile, the research further empowers 3PL fleets to judiciously oscillate between an array of energy replenishment techniques, encompassing both charging and battery swapping. Based on these insights, a Mixed-Integer Programming (MIP) model with the objective of minimizing total distribution costs incurred by the 3PL fleet is formulated. Given the characteristics of the model, a Hybrid Adaptive Large Neighborhood Search (HALNS) is designed, synergistically integrating the explorative prowess of Ant Colony Optimization (ACO) with the localized search potency of Adaptive Large Neighborhood Search (ALNS). The strategic blend leverages the broad-based solution initiation of ACO as a foundational layer for ALNS's deeper, nuanced refinements. Numerical experiments on a spectrum of test sets corroborate the efficacy of the HALNS: it proficiently designs vehicular itineraries, trims down EV energy requisites, astutely chooses appropriate energy replenishment avenues, and slashes logistics-related outlays. Therefore, this work not only introduces a new hybrid heuristic technique within the EVRP field, providing high-quality solutions but also accentuates its pivotal role in fostering a sustainable trajectory for urban logistics transportation.

A Multi-Period Charging Service Pricing Game for Public Charging Network Operators Considering the Dynamics of Coupled Traffic-Power Systems

The proliferation of electric vehicles (EVs) couples the operations of traffic systems and power systems, necessitating the interdependent traffic-power modeling to optimize the on-road EV charging decisions. In this paper, the multi-period charging service pricing interactions between multiple charging network operators (CNOs) are discussed considering the dynamics of traffic-power systems. A multi-period user equilibrium model considering mixed vehicle types is formulated to describe the possible vehicle trip transitions between different periods in urban transportation network. For the power distribution network, a convexified multi-period AC optimal power flow model is adopted for local electricity market clearing. With the dynamics of traffic-power systems, the pricing interaction between multiple CNOs is a non-cooperative game. We analyze the existence of Nash Equilibrium with fixed-point theory, and propose an iterative method based on the best response strategy to find <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon$</tex-math> </inline-formula> -Nash Equilibrium. Numerical studies based on an interdependent power-traffic system consisting of two 18-node power distribution networks and one revised Nguyen-Dupuis transportation network demonstrate that with the proposed CNOs’ charging pricing strategy, the CNOs’ service profits can increase by 1%-3% while the PDNs’ operation costs decrease about 1%-2% for the interdependent traffic-power system.

Analysis and Comparative Study of Signalized and Unsignalized Intersection Operations and Energy-Emission Characteristics Based on Real Vehicle Data

With the development of the economy, urban road transportation has been continuously improved, and the number of motor vehicles has also increased significantly, leading to serious energy consumption issues. As critical nodes in the urban road transportation network, intersections have become a focal point of research on vehicle energy consumption. To investigate whether traffic signal lights affect fuel consumption and emissions, this study analyzed the operating characteristics, fuel consumption, and emissions of intersections with and without traffic signal lights using real-world vehicle data. The data from the INTERACTION dataset for both signalized intersection VA and unsignalized intersection MA are used in the study, with a time duration of 3200 s. The VT-micro energy consumption and emissions model was applied to calculate and comprehensively analyze the vehicle flow, fuel consumption, and emissions. Additionally, the study compared the fuel consumption and emissions for different driving scenarios, including straight through, left turn, right turn, and U-turn, within a single traffic signal cycle. The results revealed that at signalized intersections, the average fuel consumption per vehicle was 26.54 L/100 km, NOx emissions were 68.76 g/100 km, and CO2 emissions were 61.07 g/100 km. In contrast, at unsignalized intersections, the average fuel consumption per vehicle was 46.88 L/100 km, NOx emissions were 149.26 g/100 km, and CO2 emissions were 107.16 g/100 km. The study indicated that for traffic volumes between 50 and 103 vehicles per 100 s, signalized intersections demonstrated better fuel consumption and emission performance than unsignalized intersections. Signalized intersections could accommodate larger traffic volumes and provide enhanced traffic safety. In conclusion, the findings of this study are important for urban traffic planning and environmental policies. They provide a scientific basis for reducing fuel consumption and emissions and improving road traffic efficiency. Due to the advantages of signalized intersections in terms of energy consumption and emissions, future urban traffic planning should consider more signal light controls to achieve energy savings, emission reduction, and improved traffic operation efficiency.

The price of quietness: How a pandemic affects city dwellers’ response to road traffic noise

Using the outbreak of COVID-19 in Singapore as a quasi-natural experiment, we investigate tenants’ changing responses to road traffic noise in the rental housing market, using 46,980 transaction records between 2006 and 2022. Our difference-in-differences estimates show that road traffic noise decreases housing rents by 3.8% immediately after the pandemic outbreak and further declines by 12.7% in the subsequent year—equivalent to 186.7 US dollars per month. The results are robust to parallel trend analysis, permutation placebo tests, and tests using alternative distance thresholds or distance to the nearest main road. Then, we adopt a machine learning text analysis of 10,425 rental housing advertisements, showing that tenants’ preference for quietness increases by approximately 10% from 2019 into 2020. The new work-from-home business model and rising traffic from delivery services can explain for this pattern. To the best of our knowledge, this is the first paper using a large volume of transaction records to quantify city dwellers’ willingness to pay for quietness in the COVID-19 context. Our results have policy implications for other nations and post-pandemic era on the interaction among urban planning, transport networks, and human settlements, and shed light on the pathway to achieve sustainable development goals.

Vulnerability Analysis of Bus Network Based on Land-Use Type of Bus Stops: The Case of Xi’an, China

The urban public transport network is closely related to urban construction and is susceptible to external influences, especially the bus network (BN). The measurement of the changes in the performance of BN under disruptions plays an important role in the development of bus systems. This paper takes the land-use type around each bus stop to modify the standard coverage range and then combines the attractive service area of the stop and the passenger flow as the opportunity coefficient to propose an improved accessibility model. Finally, the vulnerability of the BN based on the improved accessibility model in different time periods under four disruptions is analyzed. Taking BN in the central area of Xi’an as a case study, the results show that the BN is less vulnerable when stops are associated with high land-use type attractiveness, and regions with a single land-use type have high vulnerability levels. In addition, the land-use disruption causes larger-scale network vulnerability than topological disruptions. An interesting result, opposed to common sense, is found in stops within the top 10% of topological disruption failure probabilities, i.e., the BN is the most vulnerable during the off-peak night period. This study supplements the coordinated development of public transport and land use in future planning.

Retracted: Path Optimization Model for Urban Transportation Networks under the Perspective of Environmental Pollution Protection

Retracted: Path Optimization Model for Urban Transportation Networks under the Perspective of Environmental Pollution Protection

Infrastructure‐related challenges in implementing connected and automated vehicles on urban roads: Insights from experts and stakeholders

AbstractThe introduction of connected and automated vehicles (CAVs) has the potential to bring numerous advantages to urban mobility. However, many challenges for road infrastructure need to be overcome before those benefits can be achieved. This study addressed multiple dimensions of the implications of CAV deployment for road infrastructure through a comprehensive survey with 168 experts from different sectors and regions around the world. The issues are grouped into five categories: (1) key challenges of accommodating CAVs in existing urban transport networks; (2) infrastructure improvement required for shared CAV models; (3) maintenance aspects of infrastructure for CAVs; (4) implementation time of infrastructure support for CAVs; and (5) financing infrastructure upgrades to facilitate CAVs on the roads. The outcomes of the research show that there is still no consensus among the stakeholders on what should be considered to maximise CAV benefits for society as a whole. This indicates the necessity for cooperation between stakeholders to achieve the safe and efficient operation of CAVs. Overall, this study provides in‐depth insights for decision‐makers and transport planners to form policies, regulations, and guidelines regarding the future implementation of CAVs for roads before their commercialisation phase.

Refocusing MaaS approach: A brief

Lately, there are raising concerns regarding MaaS capabilities, mainly related to its impact on modal shift. In this brief, we aim to highlight the importance of refocusing the discussion around MaaS potential by arguing that it should be considered a powerful tool for public authorities to steer and optimize urban transport networks rather than a means to reduce car ownership. We believe this discussion is relevant given the rising number of MaaS skeptics and people losing faith in the concept. This approach is based on the possible accessibility gains, and the equity impacts it could bring for instance, in the global south and the gender gap in transport.

A Model Assembly Approach of Planning Urban–Rural Transportation Network: A Case Study of Jiangxia District, Wuhan, China

Planning transportation networks between urban and rural areas is of crucial importance for the integration of urban and rural development, for socio-economic connectivity, and for sustainable growth. The study offers a model assembly approach in order to logically plan an integrated urban–rural transportation network that may support the coordinated development of its living–production–ecological space. Within this approach, the ordinary least squares (OLS) linear regression analysis method is used to investigate the correlation between urban and rural areas of a transportation network and the influencing factors in the living–production–ecological space so as to objectively analyze their degree of influence. These factors are size of town, urban and rural settlements, life services, supporting transportation facilities, trunk layout, external transport links, cargo hubs, logistics and transportation, enterprise distribution, agricultural production, terrain, distribution of water systems, tourism resources, heritage preservation, and ecological protection. The analytic hierarchy method is used to assign weight to the urban and rural transportation network planning impact index system. As a result, a transportation network planning decision hierarchy model is implemented to identify suitable areas for urban and rural transportation network construction and to provide guidance and reference for planning. Jiangxia District, Wuhan, China is selected as the study area to verify the feasibility and effectiveness of the model. The findings indicate that the influencing factors of urban and rural industrial and ecological space have a significant impact on the transportation network in the research area. Planning should prioritize optimizing the central region’s transportation network structure and enhancing traffic flow between urban and rural communities, which is effectively in line with the current reality. The suggested approach is helpful in establishing case-study-specific planning and development strategies of urban and rural integrated transportation networks in the age of big data, as well as in balancing these influencing factors in living, production, and ecological spaces when planning an integrated urban and rural transportation network.