Traffic Supply Research Articles

Collaborative Optimization of Dynamic Lane Assignment and Signal Timing for Incident-Affected Intersections

Traffic accidents can lead to rapid changes in the traffic supply and demand at urban intersections, thus causing a severe traffic supply/demand imbalance during specific periods. Responding to accidents requires dynamic and accurate adjustments to optimize intersection resources collaboratively and enhance the real-time reliability and stability of traffic flows during such accidents. Currently, research on traffic incidents rarely considers real-time data-based collaborative optimization theories. Therefore, this study, supported by real-time incident detection technology and accident data, first considers the location and intensity of traffic incidents to update dynamically the changes in intersection traffic demand and supply. Subsequently, a dynamically collaborative optimization method is proposed based on lane assignment and signal timings to minimize the sum of variances of the degree of saturation of various approach lanes. Finally, various traffic demand scenarios are set, and the effectiveness of the proposed model is validated based on numerical and sensitivity analyses. The results demonstrate that compared with signal-only optimization and the highway capacity methods (HCM), the collaborative optimization method presented in this study reduces the average vehicular delay percentages by 8.54% and 16.47%, respectively. Sensitivity analysis indicates that, under various detour rates and detour modes, collaborative optimization methods have effectively mitigated the average vehicular delay at upstream intersections to varying degrees. In the context of real-time accident response, collaborative optimization methods demonstrate a capacity to promptly address the urgency of incidents occurring and maintain a sustained reduction in overall delay levels following detours.

Analysis of Multi-Modal Public Transit Competing Relationships and Evolutionary Mechanisms in Cities in Cold Regions

In order to reveal the characteristics of the competition and cooperation of multi-modal public transportation in the cities of cold areas and to study the competitive relationship of multi-modal public transportation in cold areas, the urban public transportation system in cold areas is divided into three sub-systems, namely, conventional buses, urban mass transit, and cabs, to analyze the competitive relationship and evolution mechanism of multi-modal public transportation in cities of cold areas. We construct a multi-modal public transit Lotka–Volterra competitiveness model, considering that conventional buses and urban mass transit are mainly dominant in urban public transit systems in cold regions, and introduce the competitive influence effect coefficients and cooperative influence effect coefficients to analyze the mechanism of the evolution of the competitiveness between conventional buses and urban mass transit. The data on traffic supply and demand and travel of the Harbin bus system, a typical city in China, are collected, and the simulation analysis of multi-modal competing relationship and evolution mechanism of conventional bus and urban mass transit competition is carried out, respectively, with Harbin as the research object. The results show that the constructed model can better describe the multi-modal bus competition relationship and evolution mechanism.

Exploring the provision, efficiency and improvements of urban green spaces on accessibility at the metropolitan scale

Accurately assessing urban green space (UGS) accessibility and proposing effective and specific proposals for UGS provision improvement accordingly is vital to urban development. Taking the metropolitan of Shanghai, China as the study site, this study first assessed its UGS provision by improved multiple step floating catchment area methods and multiple indexes, including UGS accessibility, theoretical capacity, potential demand, and traffic supply. Second, it investigated the impacts on citywide UGS accessibility justice for each UGS by comparing Gini index differences of citywide UGS accessibility between the conditions when exist and non-exist for each UGS. Third, it used ternary plots to explore the influence mechanism of the factors of UGS theoretical capacity, potential demand, and traffic supply on accessibility, and introduced an RGB color triangle to spatially and simultaneously display the effects of these three factors on accessibility for each UGS in Shanghai. Fourth, it assessed and classified the UGS provision efficiency for accessibility according to the relationships among the theoretical capacity, potential demand, and traffic supply factors by 3D scatter plot. Fifth, it proposed specific types and priorities of requirement for UGS improvement according to its impact on citywide UGS accessibility justice and the effect of its theoretical capacity factor on UGS accessibility. The findings showed that UGS accessibility and its three factors in Shanghai were correlative and had a spatial clustering trend in central city areas. The majority of UGSs showed positive impact on citywide accessibility justice, which went up with the values of accessibility and the three factors. Most UGSs were dominated by theoretical capacity power. The UGS provision efficiency was relatively good for most UGSs, which had relatively well-matched conditions and demands. The improvement requirements for UGSs on accessibility investigated in this study will improve UGS provision.

Data-driven evacuation and rescue traffic optimization with rescue contraflow control

In response to local sudden disasters, e.g., high-rise office or residential building fire disasters, road occupation can cause conflicts, and traffic directions may be opposite between evacuation vehicles and rescue vehicles; moreover, lane contraflow can be adopted to meet these surge traffic demands. However, lane contraflow that provides more roads for rescue vehicles reduces the traffic supply in the evacuation direction. It is unclear how to control the number of contraflow roads used by rescue vehicles to coordinate evacuation and rescue traffic operations. Here, we adjust the critical rescue traffic volume of reversing the normal road traffic direction to control rescue contraflow. Additionally, we propose a multiobjective mixed integer linear programming formulation for evacuation and rescue traffic optimization. Additionally, considering that the upper limit of the critical rescue traffic volume is unknown and that the proposed formulation includes multiple objectives and multi-priority vehicle classes, a three-stage solving algorithm is developed. Next, a large-scale evacuation and rescue traffic optimization result dataset is obtained for the Nguyen–Dupuis road network, and the impact of different rescue contraflow control plans on evacuation and rescue traffic is studied based on data-driven statistical analysis. The results show that by adjusting the optimal rescue traffic route, the critical rescue traffic volume for reversing the normal road traffic direction can reduce the interference of rescue traffic to evacuation traffic operation performance without reducing rescue traffic operation performance, and can be used to coordinate evacuation and rescue traffic operation under rescue contraflow.

Evaluation of existing underground public space supply based on the Node-Place-Experience model

The development of underground public space is considered to be a major contributor to urban sustainability. However due to the lack of customized tools for identification and assessment the effective or ineffective supply of existing underground public space has yet to receive sufficient attention resulting in frequent problems such as its undersupply and underutilization of space. Therefore this study proposes three assessment frameworks for existing underground public spaces namely traffic supply (Node) land use and activity diversity (Place) and user experience (Experience) to judge the effectiveness of the supply in 13 cases in the main urban area of Chongqing by the degree of balance of the three dimensions, and to categorize the results of the evaluation through hierarchical clustering. The study confirms that except for Nanping the other cases suffer from under-provision especially in terms of transport provision such as the low connectivity of the existing underground public space to other urban spaces. Afterward we classified the evaluated cases into four clusters: “unsustainable place with potential to be balanced,” “developing balanced with promising potential to be balanced,” “developing balanced with potential to be balanced,” “developing balanced with lower potential to be balanced” and proposed corresponding optimization paths. Our method can be used as an effective tool to identify and measure the effectiveness of existing underground public space supply and to provide a database and decision support for the optimization of future renovation of the project.

Research on Traffic Congestion Management in Xian City Based on Transit-Oriented Development

Xian City has developed rapidly in recent years, with a significant increase in automobiles. However, this has led to an exacerbation of traffic congestion and a decline in the citys traffic capacity, which has harmed the overall image and development of the city. Therefore, solving Xians traffic congestion problem is of great significance. Although some attempts have been made in the past to address traffic congestion in Xian, the problem remains a challenge due to its complexity and the emphasis on increasing traffic supply rather than reducing traffic demand. Transit-oriented development (TOD) is a concept that refers to the idea of public transportation guiding urban development. It is an important theory in the new urbanism movement and has significant guiding significance for addressing urban problems. This paper draws on the experience of managing traffic congestion under a TOD approach in foreign countries and proposes effective strategies for solving Xians traffic congestion problem, including leading transportation and urban coordinated development with a TOD approach, optimizing public transportation infrastructure, establishing a comprehensive TOD management institution, and setting up special departments and policies for funding.

Exploring Deep Learning Approaches for Short-Term Passenger Demand Prediction

An accurate short-term passenger demand forecast makes a contribution to the coordination of traffic supply and demand. Forecasting the short-term passenger demand for the on-demand transportation service platform is of utmost significance since it might incentivize empty cars to relocate from over-supply regions to over-demand regions. Yet, because spatial, temporal, and exogenous dependencies need to be evaluated concurrently, short-term passenger demand forecasting may be rather difficult. This article aims to investigate several methods that can be utilized to forecast short-term traffic demand, with a primary emphasis on deep learning approaches. We examine varying degrees of temporal aggregation and how these levels affect various architectural configurations. In addition, by analyzing 22 models representing 5 distinct architectural configurations, we illustrate the influence of varying layer configurations within each architecture. The findings indicate that the long-term short memory (LSTM) structures perform the best for short-term time series forecasting, but more complex architectures do not significantly enhance the outcomes. Moreover, considering the spatiotemporal aspects results in an improvement in the prediction of more than fifty percent. In addition, we investigate the vectorization of time, also known as Time2Vec, as a way of embedding to make it possible for a selected algorithm to recognize periodic characteristics in time series, and we show that the outcome is improved by fifteen percent.

Exploring Public Transportation Supply–Demand Structure of Beijing from the Perspective of Spatial Interaction Network

A comprehensive understanding of the relationship between public transportation supply and demand is crucial for the construction and sustainable development of urban transportation. Due to the spatial and networked nature of public transportation, revealing the spatial configuration and structural disparities between public transportation supply and demand networks (TSN and TDN) can provide significant insights into complex urban systems. In this study, we explored the spatial configuration and structural disparities between TSN and TDN in the complex urban environment of Beijing. By constructing subdistrict-scale TSN and TDN using urban public transportation operation data and mobile phone data, we analyzed the spatial characteristics and structural disparities of these networks from various dimensions, including global indicators, three centralities, and community structure, and measured the current public transportation supply and demand matching pattern in Beijing. Our findings revealed strong structural and geographic heterogeneities of TSN and TDN, with significant traffic supply–demand mismatch being observed in urban areas within the Sixth Ring Road. Moreover, based on the percentage results of supply–demand matching patterns, we identified that the current public transportation supply–demand balance in Beijing is approximately 64%, with around 18% of both excess and shortage of traffic supply. These results provide valuable insights into the structure and functioning of public transportation supply–demand networks for policymakers and urban planners; these can be used to facilitate the development of a sustainable urban transportation system.

Ride-hailing origin-destination demand prediction with spatiotemporal information fusion

Abstract Accurate demand forecasting for online ride-hailing contributes to balancing traffic supply and demand, and improving the service level of ride-hailing platforms. In contrast to previous studies, which have primarily focused on the inflow or outflow demands of each zone, this study proposes a conditional generative adversarial network with a Wasserstein divergence objective (CWGAN-div) to predict ride-hailing origin-destination (OD) demand matrices. Residual blocks and refined loss functions help to enhance the stability of model training. Interpretable conditional information is employed to capture external spatiotemporal dependencies and guide the model towards generating more precise results. Empirical analysis using ride-hailing data from Manhattan, New York City, demonstrates that our proposed CWGAN-div model can effectively predict the network-wide OD matrix and exhibits strong convergence performance. Comparative experiments also show that the CWGAN-div outperforms other benchmarking methods. Consequently, the proposed model displays potential for network-wide ride-hailing OD demand prediction.

Transport Logistics as a Tool of Interaction Between Transport Streams

State policy should be aimed at subsidizing the modernization of traffic flows, which will increase volumes and added value, should also be aimed at encouraging, reducing barriers for logistics companies that not only increase the export of goods, but also the export of services. Thus, this allows increasing GDP per capita and will lead to an increase in the instruments of interaction between transport flows. The article considers the countries that have significant potential to learn from the policies that perform best around the world. From the point of view of traffic supply, i.e. the market for logistics services, countries should be able to attract important interaction activities between traffic flows, which can contribute to further goals of transport logistics optimization. In general, it can be said that global value chains have a positive effect on the number of companies, because the more a country imports and exports a finished product, the more personnel it needs, if we are talking about services, then companies also need highly qualified personnel and thus there will be a higher level of service.

Research on Traffic Congestion Management in Small and Medium-sized Cities ——Taking the Central Urban Area of Huangshi City as an Example

Taking Huangshi City as an example, this article analyzes the current situation, causes, and strategies for managing traffic congestion in small and medium-sized cities. It puts forward measures for managing traffic congestion applicable to Huangshi City and other similar cities. These measures include strengthening road traffic planning and management and accelerating the construction of multi-level traffic facilities in traffic supply management. In traffic demand management, it proposes implementing a public transportation priority strategy, strengthening traffic diversion and illegal management.

Evolution of spatial and temporal patterns of railway container transportation: A case study of China cities.

The healthy development of railway container transport is an important part of railway freight transport and is key for promoting the green transformation of China's transport and supporting a new pattern of transport development. In this study, railway container handling station (RCHS) data, kernel density analysis, standard deviation ellipse, Herfindahl-Hirschman index (HHI), trend surface analysis (TSA), and R index were applied to examine the evolution characteristics of container transport patterns after the market-oriented reform of China's railway freight transport in 2013. The results are as follows: First, the overall scale growth trend is obvious, and the double-center effect of transport scale on the Bohai Rim region and Chengdu-Chongqing Economic Zone is evident, with the transport center of gravity moving northward. Second, the amount of attraction/occurrence is consistent in spatial distribution, and the aggregation effect of both is similar, essentially exhibiting a tendency of being high in the northwest and low in the southeast. Third, the pattern of "export-oriented in the north and import-oriented in the south" has taken shape; nearly half of cities in China have stable traffic functions, stable traffic supply, and demand relationships, and the change of functions shows that the industrial structure is constantly upgrading. This study elucidates the pattern of railway container transport in cities in China and provides empirical guidance for adjusting the functions of urban freight transport, thereby promoting the healthy development of urban freight transport and optimizing urban transport planning.

Mobility management – Hybrid model of congestion pricing

The sudden development of urbanization in cities consequently affects the degree of motorization of the population. Due to the imbalance between traffic demands and traffic supply, cities, and especially their central city zones (CZ), face the problem of traffic congestion. Management of traffic demands by applying the congestion pricing policy (CP) can significantly contribute to reducing traffic congestion and its negative impacts. However, after insufficient public and political support for the CP policy during implementation, as well as the application of the MC policy, which is based only on research in hypothetical situations, a hybrid management model was proposed. Bearing in mind the above, the objective of this study is to analyze the factors that influence the acceptability of the hybrid model, which represents combination of the previously mentioned policies (CP+MC). The obtained results indicate that the hybrid model can represents an adequate solution that would reduce the movement of passenger cars in CZ and increase the acceptance of such a policy among the public.

Dynamic Order Dispatching With Multiobjective Reward Learning

Traffic supply-demand mismatching has a severe impact on intelligent transportation systems. Fortunately, order dispatching is a promising option to mitigate the traffic supply-demand imbalance. Along this line, this article proposes the Multi-Driver Multi-Order Dispatching (MDMOD) method to make efficient order dispatching policy and enhance the experience of drivers and passengers. In the proposed MDMOD method, the Dynamic Multi-Objective Reward Learning (DMRL) algorithm is proposed to measure the driver-order-pair value, which illustrates the importance of a driver serving a specific order. A centralized matching algorithm is introduced to match all drivers and orders to maximize all driver-order-pair values. The multi-objective reward in the DMRL algorithm considers both immediate gains (i.e., pick-up distance) and future gains (i.e., the future traffic demand of order destination) to effectively improve the experience of drivers and passengers. Furthermore, by introducing the driver service level into the multi-objective reward, the “outstanding driver better reward” mechanism is realized to promote the ecological development of ride-sharing platforms. Notably, the Temporal-Graph Convolutional Network algorithm is proposed to predict the future traffic demand. Some virtual orders, which generated with the predicted future traffic demand, are dispatched to idle drivers to multiplex the traffic supply fully. A simulator is designed to test the performance of the proposed MDMOD method, experimental results demonstrate that the MDMOD method outperforms the state-of-the-art methods in terms of Average Driver Income and Order Response Rate.

The Big Data Model for Urban Road Land Use Planning Is Based on a Neural Network Algorithm.

The spatial differentiation of land use induces traffic demand and guides the construction of traffic supply; traffic conditions are an important influencing factor in determining the nature of land use, and there is a close interaction between the two. This study uses a neural network-based approach at the urban grid level to portray representative phenomena of urban development and analyze the interaction between transportation and land use. The results reflect the model's effective simulation of urban laws, and the case study reveals the differences in the laws of different cities, to guide the benign development of cities and transportation. This article firstly conducts a study on the theoretical foundation; compares the development history, planning, and design methods and practical experience of road planning and resilient planning; summarizes the experience of resilient road system design; and analyzes the future development trend, based on the above basic theoretical research, to develop research ideas and methods. Secondly, the scenario analysis method is explicitly applied to analyze various scenarios that may occur in the future development process of simulated urban roads and rank the scenarios based on the probability of occurrence. For the impact of traffic on land use, the concepts of vitality and potential are introduced, and a multidimensional long and short-term memory network (MDLSTM) model is established. The model takes into account land use lags and potential transfer and has relatively higher prediction accuracy. The results show that larger cities with urban dominant industries and tertiary industries also have higher land use potential and the more significantly influenced by traffic.

Network-wide ride-sourcing passenger demand origin-destination matrix prediction with a generative adversarial network

Accurate short-term passenger demand origin-destination (OD) matrix prediction contributes to the coordination of traffic supply and demand. This study proposes a novel generative adversarial network (GAN) named Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) to predict the network-wide ride-sourcing passenger demand OD matrix. The proposed CWGAN-GP model can not only capture internal spatiotemporal features of OD matrices, but also characterise external dependencies of OD matrices on conditional information, such as the traffic zone-based average traffic speeds, the traffic zone area, and time variables. Based on the ride-sourcing GPS trajectories from Didi Chuxing, Chengdu, China, and ride-sourcing data from the New York City, numerical results illustrate that the predicted OD matrices are in good agreement with the actual ones, and CWGAN-GP has good convergence performance by analysing the discriminator loss and the Wasserstein distance with respect to training epochs. Comparison results also validate the outperformance of CWGAN-GP compared with the other counterpart prediction methods and the reasonability of specific structures of CWGAN-GP. Thus, CWGAN-GP is concluded to be promising to predict network-wide ride-sourcing passenger demand OD matrices.

Driver Route Planning Method Based on Accident Risk Cost Prediction

The number of cars on roadways around the world continues to increase year over year. However, the imbalance between traffic supply and demand has not only brought traffic congestion but also caused serious safety problems. To reduce travel risk, this study proposes a driver route planning method based on accident risk cost prediction for connected and automated vehicles. According to the entropy weight method and an improved algorithm of K shortest paths, a route planning model with accident risk as the main optimization objective was established. Firstly, an accident risk evaluation system was built based on traffic accident data, and a quantitative prediction model of accident risk cost based on driver-, vehicle-, road-, and environment-related factors was constructed. Secondly, the entropy weight method was used to calculate the weights of each indicator to determine accident risk considering the aforementioned factors. Then, the route planning model was established, and the solution algorithm based on K shortest paths was designed to solve the optimal route by comprehensively considering accident risk cost and travel time. The accident risk index of each road section in the example road network was assigned, and the risk of the road section was quantified according to the accident risk cost model. Three candidate paths were calculated by using the path planning algorithm proposed in this study; the total risk cost is 6.19, 6.26, and 6.39, respectively; and the total travel time is 29, 29, and 31, respectively. After comparison, the optimal path and two alternative paths are obtained. The results show that the accident risk cost prediction model based on historical accident data can be used to quantify driving risk. The proposed method can help drivers in the connected and automated environment choose the optimal travel route with the lowest risk and shortest travel time and improve overall traffic safety and efficiency.

Coordinated control model for arterials with asymmetric traffic

To alleviate the congestion caused by the tidal traffic and to improve the operational performance of both directions in arterials, this paper proposes a coordinated control model for arterials with asymmetric traffic demands in both oversaturated and unsaturated directions. The model utilizes a three-step architecture: (1) A mixed-integer linear programming model is built to maximize the throughput according to the relationship between traffic supply and demand as well as the balance between inflow and outflow. (2) A quadratic programming model is constructed to minimize the vehicle delay in the oversaturated direction based on Lighthill-Whitham-Richards theory. (3) A mixed-integer linear programming model is built to maximize the variable bandwidth in the unsaturated direction by introducing the relaxation variable. Furthermore, a three-step algorithm is used to solve the model and obtain the optimal arterial signal timing. Finally, a simulation model is put forward to verify the coordinated model. The results indicate that the model meet the asymmetric traffic demand through optimizing the arterial throughput, reducing delay in the oversaturated direction and maximizing the green wave bandwidth in the unsaturated direction.

Research on the Mechanism of Influencing Factors of the Urban Road Traffic Operation State

Profound understanding of an interaction mechanism among influencing factors in the perspective of urban road traffic operation is of great significance for scientific and effective urban congestion management. In this paper, 6 indicators are proposed for the road traffic operation in the aspects of average speed of road sections, regional traffic index, and number of traffic incidents. Based on this, 4 major influencing factors and 12 measurable subinfluencing factors are proposed according to urban traffic supply and demand, and the SEM (structural equation model) is established to find out their interaction mechanism. And then, several sets of local traffic data collected in Shenzhen are used for model fitting, validation, and path analysis. The mathematical results show that all 6 indicators affiliated to the road traffic operation have a good explanation when it comes to the change of operation status. Among 4 latent variables in the traffic supply and demand aspect, the service equipment operation level and control equipment operation level can positively influence the road traffic operation status, while the urban traffic demand plays a negative role. Complex interactions among four latent variables are further pointed out. Finally, on the basis of the path coefficient relationship among the influencing factors, scientific suggestions and guidance are provided for urban road management control.

Sensitivity analysis of intercity public passenger flow based on prospect theory

The contradiction between traffic supply and demand in different periods of intercity transport corridor becomes increasingly prominent, and there is a large waste of transportation resources. In order to analyze the passenger flow sensitivity for different modes of transportation at different periods of time under the change of high-speed railway ticket price, a generalized travel prospect cost minimum assignment model is constructed. Then, the passenger flow of different travel modes in each period of the intercity passenger transport corridor is calculated, the passenger flow sensitivity model and the sensitivity rate model are also constructed. Taking Tianjin-Beijing intercity passenger transport corridor as a case study, the results show that when the ticket price reduces, the passenger flow sensitivity change rate of each mode in each period increases linearly. The increase of ticket price has little influence on all modes of transportation in each period. In the same range of ticket price fluctuation, the sensitivity of passenger flow of high-speed railway is the largest when the ticket price changes in the period of negative supply-demand balance, and the sensitivity of passenger flow corresponding to the reduction of ticket price is about twice that of passenger flow corresponding to the increase of ticket price. The research work provides a theoretical reference for optimizing the traffic structure of Tianjin-Beijing intercity passenger transport corridor.