Urban Transportation Network Research Articles

Gradual cooperative coverage models for optimally locating rain gauges on an urban transportation network

Gradual cooperative coverage models for optimally locating rain gauges on an urban transportation network

Applications of evolutionary game theory in urban road transport network: A state of the art review

A sustainable transport infrastructure is one of the pillars of a sustainable city. However, the literature indicates that urbanization, population growth, changes in population density, and motorization make it difficult for the current road transport system to meet mobility needs for a sustainable city. Traffic crashes and congestion on roads are common as a result of increasing travel times, fuel consumption, and carbon emissions, thereby reducing efficiency and sustainability of mobility systems. Managing these issues involves the interaction of multiple decision-makers, such as vehicles, pedestrians, traffic system operators, and authorities. Accordingly, these are well-suited to being analyzed under the guise of game theory. While classical game theory possesses multiple limitations, it can be argued that evolutionary game theory (EGT) models are more effective for real-world scenarios. This manuscript presents a state-of-the-art review on EGT applied to the road transportation network. The manuscript has divided the application of EGT in advancing the transportation network into multiple categories, i.e., choice-based analysis, traffic management, behavioral interactions, routing operation, and transport safety. This manuscript provides an in-depth analysis and a comparative criticism of the various proposed evolutionary game models. Finally, the manuscript discusses the challenges and provides recommendations for future research on evolutionary game models in transportation networks. These insights aim to facilitate targeted activities based on current research needs.

Cascading failures and maintenance optimization of urban transportation networks

A convenient urban transportation network facilitates a high-quality life and a high-growth economy. Due to cascading failures being a ticklish question triggering continuous road congestion, the maintenance plan is momentous to restore the urban transportation network. Considering fault edges are removed and cars slowly drive out of these edges to ease traffic congestion, a traffic distribution model is proposed to analyze the cascading failures process. To resume the transportation network, this paper proposes a maintenance optimization with minimizing maintenance time. It recovers the cascading failures from two perspectives: the intra-area maintenance model and the inter-area maintenance model. At last, a transportation network of a city in China is regarded as a case study to illuminate the feasibility of the proposed models. The results show that on the premise of dividing traffic areas, it is reasonable to adopt the intra-area maintenance plan for cascading failures. Compared with the intra-area maintenance plan, the inter-area maintenance plan saves more time.

Optimal planning of parking infrastructure and fleet size for Shared Autonomous Vehicles

Parking is a crucial element of the driving experience in urban transportation systems. Especially in the coming era of Shared Autonomous Vehicles (SAVs), parking operations in urban transportation networks may inevitably change. Parking stations are likely to serve as storage places for unused vehicles and depots that control the level-of-service of SAVs. This study presents an Analytical Parking Planning Model (APPM) for the SAV environment to provide broader insights into parking planning decisions. Two specific planning scenarios are considered for the APPM: (i) Single-zone APPM (S-APPM), which considers the target area as a single homogeneous zone, and (ii) Two-zone APPM (T-APPM), which considers the target area as two different zones, such as city center and suburban area. S-APPM offers a closed-form solution to find the optimal density of parking stations and parking spaces and the optimal number of SAV fleets, which is beneficial for understanding the explicit relationship between planning decisions and the given environments, including demand density and cost factors. In addition, to incorporate different macroscopic characteristics across two zones, T-APPM accounts for inter- and intra-zonal passenger trips and the relocation of vehicles. We conduct a case study to demonstrate the proposed method with the actual data collected in Seoul Metropolitan Area, South Korea. We find that the optimal densities of parking stations and spaces in the target area are much lower than the current situation. Sensitivity analyses with respect to cost factors are performed to provide decision-makers with further insights.

Impact of customer portrait information superiority on competitive pricing of EV fast-charging stations

The rapid development of electric vehicles (EVs) has increased the demand for public fast-charging stations (FCSs). Multiple self-interested charging service operators (CSOs) in an urban transportation network compete with each other through pricing signals to maximize their respective profits, where the information about customer demands and heterogeneous preferences work as important decision criteria. With the prevailing data transaction, the information superiority differences between CSOs may significantly affect the competitive pattern in the charging market. In this study, a Nash-Stackelberg-Nash game framework is established to investigate the strategic pricing of CSOs under different information distribution scenarios. The customer response pattern, namely, the routing and charging choices of EV drivers to pricing signals, is described via a computationally efficient extended stochastic user equilibrium model comprehensively considering heterogeneous customer preferences and decision stochasticity. Then, aiming at different data forms, both parameter-driven and sample-driven CSO pricing models are proposed. The pricing models are reformulated as a tractable single-level mixed-integer linear program, and the game equilibrium is solved alternately via Gauss-Seidel iteration. Numerical simulations are conducted to analyze the impact of single CSO information superiority and double CSO information distribution on CSO profitability and customer experience in both data forms. The influences of endogenous factors like CSO data amount and processing ability, as well as exogenous scenario parameters like customer preference distribution and perception error distribution, are discussed.

Update of the interstate standard “Reinforced concrete slabs for tramway track pavements”

Introduction. Development of urban public transport networks in Russian cities involves modernization and improvement of rail transport to increase its traveling comfort, carrying capacity and reliability. Tram transport is environmentally friendly and has a high passenger capacity. The combination as well as the intersection of rail and pneumatic transport in the urban environment is considered to be an urgent issue. In this regard, State Standard 19231 “Reinforced concrete slabs for tramway track pavements. Specifications” is needed to be revised and updated due to changes in the requirements for concrete and emergence of new classes of steel reinforcement.Aim. To update State Standard 19231 in order to bring products for tram infrastructure to the proper regulatory and technological parameters in compliance with current requirements.Materials and methods. The present study involves an analysis of existing regulatory documents in the field of reinforced concrete materials and products, as well as a bibliographical research of works and expert opinions.Results. Updating the standard resulted in the establishment of specifications that meet the current demand, both in terms of regulations and scientific research.Conclusions. The updated interstate State Standard 19231 will contribute to a higher quality design and production of reinforced concrete slabs for tramway pavements, which will surely enhance their reliability and durability.

An integrated resilience assessment model of urban transportation network: A case study of 40 cities in China

The transportation network is essential in providing daily accessibility and essential service to societies. An acceptable level of service needs to be maintained in critical infrastructures, even in the case of disruptions. We develop an integrated model to assess the resilience of urban transportation networks when exposed to different disruptions. Resilience is evaluated from perspectives of both traffic network topology and traffic flow. This model contains three parts: (1) a traffic flow simulation model to get flow distributed throughout the whole network, (2) different real-world disruptions are abstracted as random, localized, and flood disturbances, (3) functional and topological resilience are accessed by analyzing variations in travel time and connected components before and after a disruption occurs. 40 major cities are chosen to conduct resilience assessments. As the intensity of the disruption increases, common trends in the changes of functional and topological resilience values are observed and analyzed across all studied cities. In most cases, flood disruption is the least disruptive one of all three types of disruptions. Regression models are developed to estimate functional and topological resilience after random, localized, and flood disturbances. Possible resilience enhancement strategies are discussed based on analyses of regression models. This study could aid stakeholders in gaining a clear understanding of resilience not only topologically but also from the perspective of traffic flow and offer them practical strategies to enhance resilience in face of disruptions.

PERSONAL FACTORS IN MOBILITY PLANNING WITH VIRTUAL ACTIVITIES

The main reason for contemporary transport problems related to traffic congestion in urban transport networks is the excessive number of passenger cars used by people commuting to work (i.e., to places where they carry out their work duties). During the COVID-19 pandemic, a significant reduction in traffic flow was observed due to the implementation of remote work. Currently, transport problems are similar to those observed before the pandemic period. This article presents the results of research and a discussion on various factors, the recognition and analysis of which enable the virtualization of activities related to work duties. Contribution to the development of science and knowledge in the field of transport, and in particular in urban mobility planning, justifies the need for in-depth research taking into account, among other matters, objective and subjective personal factors in the assessment of social generation preferences to carry out activities in a virtual form (e.g., remote work). Proper recognition of these factors enables the development of mobility plans that can significantly reduce traffic congestion.

A Battery Electric Vehicle Transportation Network Design Model with Bounded Rational Travelers

With governments worldwide emphasizing environmental protection and the global focus on carbon reduction, the battery electric vehicle (BEV) industry has developed rapidly. An urban transportation network with BEVs as the main form of transportation will soon become mainstream. Motivated by the abovementioned background, a BEV transportation network design problem is investigated, and a network design model is established. The model aims to minimize the system travel time of BEV transportation networks and optimize the government’s lane expansion scheme (the location and number of lanes) under a limited budget. To consider the travel characteristics of BEV drivers, the charging time, range anxiety, and bounded rationality factors are simultaneously incorporated into the model. A heuristic algorithm is designed based on the active set algorithm to obtain the local optimal solution to the actual-scale problem. Moreover, a cutting-plane method is used to convert the original problem into a different form, and a column generation technique is embedded in the abovementioned algorithm to avoid the enumeration of paths. Sensitivity analyses of different levels of rationality of BEV drivers and government investment scales are performed. The experimental results demonstrate that the model and algorithm can effectively solve the problem and provide decision support for the government in formulating transportation infrastructure construction policies.

A hybrid returns to scale-DEA model for sustainable efficiency evaluation of urban transportation systems

The urban transportation network has an undeniable role in addressing the economic, social and environmental issues caused by the traffic. Transportation managers seek to use the existing facilities and capacities in an optimal way to increase customer satisfaction. Therefore, it is necessary to develop an approach to evaluate the performance of the urban transportation system to provide service to citizens effectively. This study develops an approach based on the extended version of the data envelopment analysis (DEA) model to measure the nonradial efficiency and super-efficiency of metro-stations considering the sustainability concept. The developed non-radial DEA model considers the hybrid returns to scale the form of technology by combining constant and variable returns to scale assumptions to improve its applicability to identify efficient and inefficient stations. This DEA model also incorporates the non-discretionary inputs and different types of outputs (i.e. undesirable, negative and non-negative) to improve discrimination power and the ability to interpret the results. The findings help decision-makers identify super-efficient stations as a benchmark for future planning and finding the best location to construct metro-stations. Furthermore, this research enables managers to optimally use the resources to increase the transferred passengers, reduce customer dissatisfaction and optimise the annual profit.

Understanding the mobility of public transport systems based on weighted multiplex networks

The structure and properties of urban public transport networks are important for understanding the mobility of public transport systems. However, most studies have ignored the impact of shared mobility and lacked the support of large-scale empirical data when exploring the mobility of urban public transport networks. Using multi-mode travel data, this study constructs a three-layer multiplex network and assigns weights to reflect the transport capacity of different modes (bus, metro, and shared bike) based on ridership and travel distances. The case study was conducted on the public transport system of Shenzhen, China. The results show that the degree distribution and edge weight distribution of the multiplex network exhibit distinct scale-free characteristics. The effect of the multiplex network model on single-layer networks suggests that cooperation between networks significantly improves the capacity and efficiency of urban public transport systems. Regression analysis reveals the effects of the urban population and land use on the demand for public transport services. The results show an intrinsic association between the public transport system and urban land use, which will facilitate the prediction of public transport demand and serve the sustainable development of cities.

Evaluation of Bus Lane Layouts Based on a Bi-Level Programming Model—Using Part of the Qingshan Lake District of Nanchang City, China, as an Example

Bus lanes have gradually become an indispensable infrastructure for the development of urban public transportation networks. A bi-level programming model is used herein to compare the total travel time of social vehicles and buses before and after the bus lane is set up to judge whether the bus lane layout plan is scientific and reasonable considering the road network. The model’s effectiveness is verified using areas of Qingshan Lake in Nanchang City, and the operational efficiency of one bus lane before and after establishing the dedicated bus lane is analyzed. The case results show that the bus lane layout evaluation needs to consider the traffic benefits of a specific road and also judge whether the total travel time of the network traffic volume is improved from the macroscopic road network level. The research results provide theoretical support for the rational layout of bus lanes and are of practical significance for prioritizing the development of public transportation and promoting the sustainable development of urban transportation.

New York City Taxi Trip Duration Prediction Using Machine Learning

Abstract: Given the complexity of urban transportation networks and the multiple variables that might affect journey times, estimating the length of cab rides in New York City (NYC) is a difficult process. In this study, we provide a unique method for resolving this issue that makes use of machine learning techniques and a wide range of attributes gleaned from taxi trip data. We start by gathering a sizable collection of historical records of NYC taxi trips,providing specifics like pick-up and drop-off points, timestamps, and lengths of trips. To deal with outliers, missing values, and geographical and temporal irregularities, we preprocess the data. Furthermore, we design a broad range of characteristics, such as geographic coordinates, time of day, day of the week, and weather conditions, to capture the spatial, temporal, and contextual elements of each journey. Then, using gradient boosting methods, we create a prediction model that efficiently uncovers the intricate patterns seen in the data. We carefully adjust the model's hyperparameter to enhance performance and use cross-validation techniques to guarantee resilience. In addition, we apply ensemble techniques to enhance prediction precision and minimise model bias. We conduct lengthy tests on a held-out test set and compare the performance of our model to a number of baseline techniques frequently employed in triptime prediction in order to assess the efficacy of our suggested strategy. The outcomes show that our strategy works better than the competition, with lower prediction errors and higher accuracy. We also do interpretability assessments to learn more about the variables that have the most impact on estimates of trip time. Our results demonstrate the potential of feature engineering and M L approaches for precise and trustworthy taxi trip length prediction in NYC. The suggested method not only helps taxi service companies by allowing them to more accurately predict journey lengths, but it also improves customer experience by giving more precise travel time estimates. Additionally, our approach may be used as a starting point for future studies in the field of urban transportation prediction, enabling better efficiency and planning in urban mobility networks.

Passenger‐perception dynamic ridesharing service based on parallel technology

AbstractWith the emergence of new information and communication technology, mobile‐based ridesharing services have received more attention and have improved. They offer timely and convenient service and exert an enormous influence on pollution and congestion by allowing passengers to share overlapping routes. However, ridesharing services have also been discussed and called into question by people with respect to their attractiveness, their comfort, and passenger safety. This paper proposes a ridesharing algorithm with a perceived mechanism by introducing multiple dimensions of perceived value to overall assess passengers’ perceptions. The approach involved can continuously track the psychological changes in passengers during travel, capture the dynamic and comprehensive perceptions of passengers, and make real‐time adjustments on this basis. Furthermore, the proposed partition‐based parallel strategy can ensure high‐quality services and further enhance computational efficiency. Finally, numerical experiments are conducted based on the urban transportation network and the actual trip data from Manhattan to evaluate the performance of the proposed algorithm. The results show that it is important to effectively capture the passengers’ perception of ridesharing services and apply them to optimization mechanisms, which will contribute to improving the perception of ride experiences and attracting potential passengers.

Decision support algorithm for intermodal transport networks: Urban aerial cableway systems

We propose a decision support algorithm to solve the service policy of urban cableway systems, which are used as a transport mode to address the First-Last Mile problem for intermodal urban public transport networks. Our work develops a stochastic discrete-event model for computing the queuing performance of the public transport networks and evaluates the effects on the passenger waiting time over a commercial working day, considering different service policies: (i) previous policy; (ii) current policy; and (iii) proposed policy. We provide an embarking procedure for urban cableway lines to aid transport operators in managing queues on platforms during rush hours. Finally, a set of large-scale Monte-Carlo simulations was conducted using a real multimodal network of a large-size metropolitan city (3.8 million inhabitants) to evaluate the effectiveness of different service policies.

Frequent Congestion Detection Model Based on Critical Intersection Identification

Intersection information in urban transportation networks is critical to the safe and efficient operation of road traffic. The controllability of the road network can be increased by controlling critical intersections during peak traffic periods. On the one hand, based on the improved intra- and inter-level correlation description matrix of time steps, a directed supra-adjacency matrix (DSAM) temporal network model is built to verify the validity of the model using the road network data of urban core areas and compare the results with those obtained from static critical intersection identification. The results show that the DSAM model has a phase continuity feature in ranking the intersection importance, which has richer connotation than the critical intersection identification results based on a single indicator and has better consistency at different time granularity. On the other hand, based on the DSAM model to identify critical intersections of road network, short-term traffic flow prediction is performed with the help of a road network adjacency matrix, and the results show that the Chebyshev diagram convolution neural network (ChebNet) has the best performance. It can be used in the detection of recurrent congestion of urban road networks, and corresponding control measures are made in shorter time steps, which can prevent the possibility of widespread congestion and reduce the blockage of the whole road network. In summary, the model can be used for critical intersection identification and congestion detection in urban road networks.

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 consumer surplus, welfare and profit enhancing strategy for improving urban public transport networks

We show that a novel pricing system can help resolve a series of perennial problems evident in the deregulated British urban public transport market that have impeded urban growth, access equality and environmental ambitions. A two-stage pricing system, with operators setting their multi-operator service ticket prices collusively in one stage and their single-operator ticket prices independently in the other, offers potential consumer surplus, profit and welfare gains over, what we characterise as, the free-market ‘Status Quo’. The proposed win–win pricing system can also support a larger number of operators and services with potential additional welfare gains. We also compare the proposed system against a multi-operator ticketing card (MTC) scheme, permitted in the UK under the Block Exemption. The Block Exemption allows collusive pricing on a limited basis but is due to expire and is under statutory review, making this is a timely contribution. We show, whilst the MTC offers higher welfare when all regimes provide the same number of services, the proposed system can support a larger number of operators in the presence of fixed costs, which can reverse the welfare ranking in its favour. A calibration exercise indicates the market may be operating in the region where the proposed system can dominate the ‘Status Quo’ in profit, consumer surplus and welfare terms and support a larger network than the ‘Status Quo’ or MTC with further welfare gains. The resulting higher public transport patronage may also offer further indirect benefits via reduced pollution, congestion and accidents. Improved transport efficiency may have urban density advantages, especially in Britain’s second-tier cities which do not tend to benefit from extensive public transit rail and underground networks, with associated agglomeration effects contributing to the current levelling-up priority. Given the salience amongst developed countries of the private aspect of urban public transport in Britain, along with an unresolved private vs public debate, this issue is of potential interest to urban planners and policymakers beyond the UK.

Generalized User Equilibrium for Coordination of Coupled Power-Transportation Network

The increasing proliferation of electric vehicles (EVs) tightens the interaction between urban power distribution network (PDN) and transportation network (TN). This paper proposes the generalized user equilibrium (GUE) method for the coupled power-transportation network (CPTN) operation. First, the GUE concept is proposed to describe the steady traffic flow distribution as an extension of user equilibrium (UE), which could no longer exist due to the constraints placed by PDN operation on TN. Second, the GUE-based coordination model is established for CPTN with PDN generation scheduled and TN traffic assigned simultaneously. Finally, a decomposition method is developed to solve the model efficiently, which is converted into a master problem and a series of feasible/optimal path generation subproblems for individual origin-destination (O-D) pairs. The case studies on a real-world network have verified the effectiveness of the proposed model and method. The results demonstrate the necessity of GUE and its potential in improving the CPTN operation.

Perceptions of Stress Among Pedestrians in Fully Automated Vehicle Transportation Systems

Fully automated vehicle (FAV) technology is recognized to offer a variety of benefits, such as increased safety, a drop in emissions, and better quality of life for people with disabilities or without a driving license. Despite the benefits, it has not been widely adopted. Demographics and user personalities play a large role in technology adoption, but nonusers’ involvement is often overlooked. Considering the current attention on FAV technology and its users, pedestrians (who do not use it) are understudied. In future, FAVs may be deployed once they have been proven safe and accepted by the public. This study aimed to provide insights into how pedestrians’ emotional perceptions might influence their adoption of FAVs. Within-subject research was conducted using virtual reality-based videos to assess pedestrians’ perceptions of stress. Videos showed identical first-person-view walking scenarios of various spatial contexts and activities in urban transportation networks comprising all-human-driven vehicles (HDVs) and futuristic all-FAVs. The results indicated that pedestrians’ perceptions of stress differed significantly by vehicle type, spatial context, and activity. Further, they differed significantly by gender and whether walking was perceived as safe. Walking in all-FAV transportation systems was perceived as more stressful than walking in all-HDV systems. Women tended to report higher levels of stress. Crossing roads and shared pathways were also associated with higher levels of perceived stress. Research related to pedestrians’ emotional perceptions could have important implications for future policymaking and urban planning.