Urban Transportation Network Research Articles

End-to-end heterogeneous graph neural networks for traffic assignment

The traffic assignment problem is one of the significant components of traffic flow analysis for which various solution approaches have been proposed. However, deploying these approaches for large-scale networks poses significant challenges. In this paper, we leverage the power of heterogeneous graph neural networks to propose a novel end-to-end surrogate model for traffic assignment, specifically user equilibrium traffic assignment problems. Our model integrates an adaptive graph attention mechanism with auxiliary “virtual” links connecting origin–destination node pairs, This integration enables the model to capture spatial traffic patterns across different links, By incorporating the node-based flow conservation law into the overall loss function, the model ensures the prediction results in compliance with flow conservation principles, resulting in highly accurate predictions for both link flow and flow-capacity ratios. We present numerical experiments on urban transportation networks and show that the proposed heterogeneous graph neural network model outperforms other conventional neural network models in terms of convergence rate and prediction accuracy. Notably, by introducing two different training strategies, the proposed heterogeneous graph neural network model can also be generalized to different network topologies. This approach offers a promising solution for complex traffic flow analysis and prediction, enhancing our understanding and management of a wide range of transportation systems.

Effect of interlayer dependence strength on continuous to discontinuous transitions in multi-layer networks

Studies on multilayer networks have aroused great interest in recent years because they allow a deeper description of the complexity present in many real systems. In this study, we introduce a dependency strength controller into multilayer interdependent networks, refining the traditional models that often inadequately represent cascading failures in real-world systems like urban transportation networks. This refined model enables a more realistic depiction of failure dynamics, where a node’s failure leads to its dependent node’s failure with a probability λ or survival with 1−λ. We derive exact equations that describe this process in a multilayer network and its structural evolution, and solve them numerically. We found the existence of a critical transition from continuous to discontinuous percolation as the strength of the dependence between layers increases. The critical percolation thresholds for these transitions are numerically derived for two-layer Erdős-Rényi networks and two-layer scale-free networks. This enhanced understanding of complex systems, incorporating initial disturbances and varying dependency strengths, contributes to designing more resilient multilayer complex systems.

Distributed Peer-to-Peer Coordination of Hierarchical Three-Phase Energy Transactions Among Electric Vehicle Charging Stations in Constrained Power Distribution and Urban Transportation Networks

Distributed Peer-to-Peer Coordination of Hierarchical Three-Phase Energy Transactions Among Electric Vehicle Charging Stations in Constrained Power Distribution and Urban Transportation Networks

A Methodology for a Comprehensive Evaluation of an Urban Transport Network Structure Using Geographical Information Systems (GIS)

Abstract The transportation network is the most important component of an urban infrastructure. The efficiency of a region’s transportation system can be understood by the effectiveness of its transport network arrangement. The current study employs Geographical Information Systems (GIS) to assess the structure of a transport network in various clusters of the Hyderabad Metropolitan Area (HMA). The study focuses on a thorough assessment of the transport (or) road network structure in terms of various criteria such as connectivity, accessibility, maturity, and development. The study also categorizes each Transport Network Structural Evaluation Criteria (TNSEC) level as “very low, low, medium, high, or very high”. The goal of this research is to create a Road Network Structural Performance Index (RNSPI), which is usually used to measure the efficiency of a study area’s transportation network structure. The study’s findings serve as a foundation for city transportation planners to put efforts in the planning to enhance network facilities in the study region. The study also investigates the utility of Arc GIS software in assessing the structure of an urban transport network.

Enhanced-GA supports traffic signal optimizations and protects the urban environment

Much current research on traffic signal optimization often neglects the impact of environmental factors in urban areas. This can result in suboptimal solutions that do not consider the effects of traffic on air pollution and the overall urban environment. To address this issue, this article proposes a solution that combines Enhanced GA with a comprehensive framework for considering environmental factors in traffic signal optimization. By optimizing traffic signal timings and minimizing emissions, the proposed solution aims to reduce congestion and improve urban transportation networks' efficiency while protecting the environment. The proposed approach uses a set of optimization algorithms and assumptions to generate a comprehensive framework for traffic signal optimization. These algorithms and assumptions consider environmental factors such as air quality and the impact of traffic on the local ecosystem. Moreover, this article provided the enhanced genetic algorithm operators and suggested model formulation that could be applied in other research on traffic signal optimization directly to reduce calculation times and increase the efficiency of the novel suggested models

Advancing Traffic Signal Management: Integrated Strategies for Optimal Efficiency

This piece explores the integration of self-healing materials, predictive maintenance strategies, and real-time signal synchronization techniques in traffic signal management. Through case studies from cities like Singapore, Copenhagen, Los Angeles, and others, we examine their utilization, benefits, and challenges. We highlight disparities in urban environments and technological implementations, focusing on sustainability, data security, and stakeholder engagement. Emphasizing innovation and flexibility, our discussion underscores the crucial role of integrated methodologies in crafting safer, more efficient, and adaptable urban transportation networks.

Towards explainable traffic signal control for urban networks through genetic programming

The increasing number of vehicles in urban areas draws significant attention to traffic signal control (TSC), which can enhance the efficiency of the entire network by properly switching the phases of each signalized intersection. Fixed and max-pressure methods are commonly used in TSC systems owing to their high simplicity and good interpretability, but they respectively lack dynamic adaptability and automatic rule generation, possibly leading to low solution accuracy in complicated traffic environments. Meanwhile, meta-heuristic and black-box learning methods meet challenges in practice such as extensive computational time and poor interpretability. To this end, this paper proposes a new TSC method based on Genetic Programming (GP) to generate descriptive score rules automatically for switching phases of all signalized intersections in an urban transportation network. In the proposed method, switching phases of each signalized intersection type is formulated as a symbolic regression problem, and effective primitives are defined to facilitate GP to solve the problem. Experiments have been conducted on both synthetic and real-world networks. The results have validated the effectiveness of our proposed GP based method compared to several state-of-the-art TSC methods in terms of accuracy and interpretability.

A dynamic system optimal dedicated lane design for connected and autonomous vehicles in a heterogeneous urban transport network

Numerous contemporary studies have posited that connected and autonomous vehicles (CAVs) hold the potential to enhance traffic safety and augment efficiency substantially. One widely discussed approach to optimize CAV operations within urban traffic networks involves the implementation of dedicated lanes (DLs). This study aims to assist system planners in optimally deploying DLs within heterogeneous urban traffic networks of CAVs and Human-Driven Vehicles (HDVs). In pursuit of this objective, we have introduced a multi-class dynamic traffic assignment framework that enhances network performance and offers insights into traffic dynamics. Additionally, our methodology considers dynamic routing behaviour while devising DLs, formulating and approximating the problem as a mixed-integer linear program (MILP). The resulting strategy delineates the temporal and spatial aspects of the deployment of DLs for CAVs, specifying the quantity and locations of these lanes. Subsequently, we assessed our framework using test-bed networks of varying sizes and demand profiles, evaluating the solution’s quality and the model’s adaptability to diverse traffic conditions. Our findings indicate that implementing DLs for CAVs can bolster vehicular throughput across the network while neglecting dynamic capacity variation in mixed traffic may yield misleading outcomes.

Predicting Bus Travel Time in Cheonan City Through Deep Learning Utilizing Digital Tachograph Data

Urban transportation systems are increasingly burdened by traffic congestion, a consequence of population growth and heightened reliance on private vehicles. This congestion not only disrupts travel efficiency but also undermines productivity and urban resident’s overall well-being. A critical step in addressing this challenge is the accurate prediction of bus travel times, which is essential for mitigating congestion and improving the experience of public transport users. To tackle this issue, this study introduces the Hybrid Temporal Forecasting Network (HTF-NET) model, a framework that integrates machine learning techniques. The model combines an attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, enhancing its predictive capabilities. Further refinement is achieved through a Support Vector Regressor (SVR), enabling the generation of precise bus travel time predictions. To evaluate the performance of the HTF-NET model, comparative analyses are conducted with six deep learning models using real-world digital tachograph (DTG) data obtained from intracity buses in Cheonan City, South Korea. These models includes various architectures, including different configurations of LSTM and GRU, such as bidirectional and stacked architectures. The primary focus of the study is on predicting travel times from the Namchang Village bus stop to the Dongnam-gu Public Health Center, a crucial route in the urban transport network. Various experimental scenarios are explored, incorporating overall test data, and weekday and weekend data, with and without weather information, and considering different route lengths. Comparative evaluations against a baseline ARIMA model underscore the performance of the HTF-NET model. Particularly noteworthy is the significant improvement in prediction accuracy achieved through the incorporation of weather data. Evaluation metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE), consistently highlight the superiority of the HTF-NET model, outperforming the baseline ARIMA model by a margin of 63.27% in terms of the RMSE. These findings provide valuable insights for transit agencies and policymakers, facilitating informed decisions regarding the management and optimization of public transportation systems.

Construction and Characteristics Analysis of the Xi’an Public Transport Network Considering Single-Mode and Multi-Mode Transferring

The connectivity of the urban public transport network and the convenience of transfers between modes of transit are important factors that affect whether passengers choose public transport. Identifying the key nodes that affect network connectivity, direct access, transfers, and clarifying the main factors that restrict the network efficiency play important roles in improving the efficiency of the public transport network and establishing a “green city”. On this premise, this paper constructs two single-layer networks and a composite network that can reflect the transfer relationship between ‘bus–bus’, ‘metro–metro’, and ‘metro–bus’ based on the method of Space-P. The composite network realizes the integration study of homogeneous and heterogeneous stops, lines, and transfer relationships in the public transport network. At the same time, five kinds of centrality indexes are applied to the transport transfer network, and the significance of these indexes in the network is explained. Through the comprehensive analysis of these five types of indexes, the key nodes affecting the network connectivity, direct access and transfer efficiency, can be identified more accurately. Taking the public transport network of Xi’an as an example, the structural characteristics of the networks, including scale-free and small-world characteristics, were empirically analyzed. The main stops that play important roles in networks were identified based on the integrated centrality, degrees, and weight degrees. The research results showed the following: (1) Xi’an’s metro network, bus network, and metro–bus composite network all have scale-free and small-world characteristics. (2) The influence of the key stops of the metro network is concentrated, while the influence of the key stops of the bus network is scattered. (3) The public transport network in the first ring road area of Xi’an has the highest degree of direct access, and the core areas of the south, west, and north of Xi’an also have high direct access. However, the direct access in the area east of Xi’an is slightly lower. (4) Xi’an’s bus transport network covers a large area, showing the characteristics of a dual-core “central + southern” network. (5) The metro–bus composite network demonstrates a closer connection between stops and a more balanced network. (6) Finally, the degree of direct access to stops in the bus transport network and metro transport network shows the characteristics of “the single core is dominant, and the circle diffusion weakens step by step”.

Safety-oriented urban pavement design and evaluation: integrating microscopic simulation and tyre-pavement friction

ABSTRACT Tyre-pavement friction significantly impacts traffic safety performance, yet integrating these topics is often overlooked in pavement engineering. This paper addresses this gap by utilising microscopic traffic simulation and surrogate measures of safety to assess vehicular conflicts severity in diverse urban scenarios and establish friction thresholds for practical implementation. A pre-construction materials selection approach is proposed, employing a predictive model for tyre-pavement friction. Findings reveal that combinations of fine-graded mixtures and low surface texture mineral aggregates can lead to increased conflict severity in urban environments. Furthermore, a post-construction texture assessment using 3D computer vision was applied for verifying compliance with friction thresholds for asphalt pavement surfaces. This study emphasises the potential for cost-effective integration of traffic simulation and safety analysis to an enhanced urban pavement design and evaluation. By incorporating safety-based tyre-pavement friction considerations, decision-making related to materials selection and friction conditions can be improved, leading to safer urban transportation networks.

Real-Time Traffic Light Optimization Using AI and IOT

Abstract: Urban traffic congestion is a significant challenge globally, impacting transportation efficiency, environmental sustainability, and urban liveability. Traditional traffic control systems often struggle to adapt to changing traffic dynamics, leading to increased congestion and delays. This paper presents a novel traffic management system developed by our team, leveraging cutting-edge technologies such as computer vision, artificial intelligence (AI), and the Internet of Things (IoT). Deployed at intersections, our system utilizes real-time CCTV feeds for traffic analysis, employing advanced image processing and machine learning algorithms, including YOLO V7, to dynamically assess traffic density. These insights inform adaptive adjustments to traffic light timings, with the aim of reducing congestion, enhancing transit efficiency, and mitigating pollution. Our signal switching algorithm, incorporating dynamic logic developed by our team, iteratively adjusts signal timings based on real-time traffic conditions, ensuring responsive and efficient traffic flow management. Through the integration of our innovative technologies, our system seeks to revolutionize urban transportation networks, promoting smarter, more adaptive, and sustainable urban environments.

Developing urban infrastructure constructions for increasing e-commerce sales: the moderating roles of aging population

PurposeThe aging population, a higher proportion of older adults (aged 65+), is considered a global and severe problem, while the information systems (IS) literature on detecting the relationship between the aging population and the development of electronic commerce (e-commerce) is limited and insufficient. Hence, the main objective of this paper is to examine whether an aging population can moderate the effect of infrastructure constructions on e-commerce sales and whether an aging population can affect e-commerce sales.Design/methodology/approachTo investigate the relationship between the aging population and e-commerce sales, this study proposes two potential influential mechanisms: moderating the effects of infrastructure development on e-commerce sales and direct influence. Subsequently, a sample of 31 Chinese provinces from 2013 to 2019 is utilized to conduct regression analyses in order to examine these hypotheses.FindingsThe findings suggest that the development of urban transportation infrastructure and network constructions can significantly contribute to the enhancement of e-commerce sales, and the influence cannot be affected by aging population. Furthermore, it is noteworthy that an aging population can have a positive effect on e-commerce sales.Practical implicationsThe findings can inform future infrastructure constructions by assessing the potential of infrastructure projects to boost e-commerce sales and examining whether this effect varies in an aging population context.Originality/valueThe findings substantiate the pivotal role of older adults in the e-commerce industry. Moreover, the obtained results establish a positive relationship between an aging population and e-commerce sales, thereby offering diverse perspectives on existing theories.

Artificial intelligence of things for smart cities: advanced solutions for enhancing transportation safety

In the context of smart cities, ensuring road safety is crucial due to increasing urbanization and the interconnected nature of contemporary urban environments. Leveraging innovative technologies is essential to mitigate risks and create safer communities. Thus, there is a compelling imperative to develop advanced solutions to enhance road safety within smart city frameworks. In this article, we introduce a comprehensive vehicle safety framework tailored specifically for smart cities in the realm of Artificial Intelligence of Things (AIoT). This framework seamlessly integrates a variety of sensors, including eye blink, ultrasonic, and alcohol sensors, to bolster road safety. The utilization of eye blink sensor serves to promptly detect potential hazards, alerting drivers through audible cues and thereby enhancing safety on smart city roads. Moreover, ultrasonic sensors provide real time information about surrounding vehicle speeds, thereby facilitating smoother traffic flow. To address concerns related to alcohol consumption and its potential impact on road safety, our framework incorporates a specialized sensor that effectively monitors the driver’s alcohol levels. In instances of high alcohol content, the system utilizes GPS and GSM technology to automatically adjust the vehicle’s speed while simultaneously notifying pertinent authorities for prompt intervention. Additionally, our proposed system optimizes inter-vehicle communication in smart cities by leveraging Li-Fi technology, enabling faster and more efficient data transmission via visible light communication (VLC). The integration of Li-Fi enhances connectivity among connected vehicles, contributing to a more cohesive and intelligent urban transportation network. Through the structured integration of AIoT technologies, our framework lays a robust foundation for a safer, smarter, and more sustainable future in smart city transportation. It offers significant advancements in road safety and establishes the groundwork for further enhancement in intelligent urban transportation networks.

Non-Iterative Coordinated Optimisation of Power–Traffic Networks Based on Equivalent Projection

The exchange of sensitive information between power distribution networks (PDNs) and urban transport networks (UTNs) presents a difficulty in ensuring privacy protection. This research proposes a new collaborative operation method for a coupled system. The scheme takes into account the schedulable capacity of electric vehicle charging stations (EVCSs) and locational marginal prices (LMPs) to handle the difficulty at hand. The EVCS hosting capacity model is built and expressed as the feasible area of charging power, based on AC power flow. This model is then used to offer information on the real schedulable capacity. By incorporating the charging loads into the coupling nodes between PDNs and UTNs, the issue of coordinated operation is separated and becomes equal to the optimal problem involving charging loads. Based on this premise, the most efficient operational cost of PDNs is transformed into a comparable representation of cost information in PDNs. This representation incorporates LMP information that guides charging decisions in UTNs. The suggested collaborative scheduling methodology in UTNs utilises the collected projection information from the static traffic assignment (STA) to ensure data privacy protection and achieve non-iterative calculation. Numerical experiments are conducted to illustrate that the proposed method, which uses a smaller amount of data, achieves the same level of optimality as the coordinated optimisation.

System capacity model and algorithm for urban multimodal transport network with transfer

ABSTRACT This paper proposes a method for calculating transport networks capacity in dealing with multimodal transfers. Multimodal networks are represented by a modified ‘supernetwork’, while the passenger’s travels are defined as ‘superpaths’. Within this framework, the relation between the travel demand from O-D matrices and the resulting link flows in the supernetwork is modelled as a relationship matrix to describe urban mobility by using a logit-based stochastic user equilibrium. Based on this relationship matrix, an approximate iteration algorithm (AIA) is developed. Our numerical results show that the AIA performs better than the sensitivity analysis-based algorithm (SAB) and genetic algorithm (GA) regarding the execution-time, and that the capacity of multimodal transport networks can be underestimated if the combined travels are neglected.

Identification of critical links based on the optimal reliable path in stochastic traffic networks.

In urban stochastic transportation networks, there are specific links that hold great importance. Disruptions or failures in these critical links can lead to reduced connectivity within the road network. Under this circumstance, this manuscript proposed a novel identification of critical links mathematical optimization model based on the optimal reliable path with consideration of link correlations under demand uncertainty. The method presented in this paper offers a solution to bypass the necessity of conducting a full scan of the entire road network. Due to the non-additive and non-linear properties of the proposed model, a modified heuristic algorithm based on K-shortest algorithm and inequality technical is presented. The numerical experiments are conducted to show that improve a certain road link may not necessarily improve the overall traffic conditions. Moreover, the results indicate that if the travel time reliability is not considered, it will bring errors to the identification of key links.

Stackelberg Game-based Coordinative Dispatch of EV Charging Stations and Urban Distribution Systems Considering Temporal-spatial Dynamic Traffic Flow

This paper proposes a Stackelberg Game-based coordinative dispatch model of electric vehicle charging stations (EVCSs) and urban distribution systems (UDS) considering temporal-spatial dynamic traffic flow. The model is formulated hierarchically and consists of an upper-level problem and two lower-level problems. Firstly, a detailed EVCS dispatching model considering both electricity prices and charging service prices is developed in the upper-level. Then the electricity pricing model of UDS and travel decision model of EV users are proposed in the lower model. Particularly, a temporal-spatial dynamic model is developed to capture the traffic flow evolution in urban transport network preciously. EVCS, UDS operator (DSO) and EV users can make decisions independently as different stakeholders. Finally, a multi-layer iterative framework is designed to capture the tripartite benefit equilibrium of distribution grid, EV charging stations, and EV users, and to realize the effective solution of the model. Numerical studies based on a modified IEEE 33-bus system demonstrate the effectiveness and accuracy of our proposed approach.

A Stochastic Charging Station Deployment Model for Electrified Taxi Fleets in Coupled Urban Transportation and Power Distribution Networks

A Stochastic Charging Station Deployment Model for Electrified Taxi Fleets in Coupled Urban Transportation and Power Distribution Networks

Exploring the Impact of Accessibility on Place Attachment in Urban Public Open Spaces: A Case Study of Jiamusi City, China

Urban public open spaces are crucial for residents’ well-being, yet accessibility issues persist, affecting activities and social interactions. To this end, we take the main urban area of Jiamusi City, the most northeastern city in China, as an example. We start by examining both spatial and perceptual dimensions, using the Gaussian two-step moving search method to measure spatial accessibility, combining online data with multi-source data from questionnaires. Furthermore, we utilize structural equation modeling to explore the impact of accessibility on place attachment within urban public open spaces. The results show that (1) accessibility has a positive effect on place attachment, while place satisfaction plays a mediating role. (2) There were significant differences in spatial accessibility across modes of travel. Car travel had the best spatial accessibility, followed by bicycle, and walking had the lowest spatial accessibility. (3) Perceived accessibility and spatial accessibility differ, but perceived accessibility it is more persuasive in explaining and illustrating place attachment. Reducing travel costs and meeting the user needs will effectively increase place attachment and place identity. (4) Social factors such as age, education, and residency length also affect accessibility, satisfaction, and attachment. By understanding the impact of accessibility on place attachment, this study helps urban planners to better design urban spatial layout and transportation networks. It enhances people’s attachment to specific places by improving the convenience of traveling, promotes sustainable urban development, and enriches the discourse on the enhancement of psychological motivation in urban public open spaces.