Traffic Performance Index Research Articles

Quantitative Estimation and Analysis of Spatiotemporal Delay Effects in Expressway Traffic Accidents

Expressway traffic accidents often result in severe congestion, with their unpredictable nature complicating timely and effective response measures. This paper presents a comprehensive method for accurately estimating and analyzing the spatiotemporal delay effects of expressway accidents through the integration of multi-source geographic data. The innovation lies in utilizing real-world vehicle trajectory data, combined with a Traffic Performance Index (TPI), to quantitatively assess delay impacts. By applying spatial clustering and hotspot detection techniques, we investigate the distribution patterns of delays and further employ a Spatial Error Model (SEM) to examine the relationships between accident characteristics and associated delay effects. Using expressway accident data and vehicle trajectory records from Hunan Province, the results demonstrate that the TPI-based approach effectively captures the duration, extent, and severity of traffic delays. Moreover, significant correlations are identified between delay impacts and specific accident characteristics, such as accident type, road type, road environment, pre-accident vehicle speed, and secondary accidents. This approach provides traffic management authorities with actionable insights into the overall roadway impact, facilitating targeted emergency response strategies and informing road usage policies tailored to the characteristics of accident impacts, thus helping to mitigate future risks.

Analysis of Traffic Characteristics in Beijing --Based on the Traffic Performance Index

This study aims to deal with the increasingly serious urban traffic congestion problem, taking Beijing as a case study. With the increase in population, traffic congestion has become a serious problem affecting peoples life quality and social and economic development. This study uses the time series analysis and uses the ARIMA (1,0,0) (0,1,1)12 model to forecast the traffic performance index (TPI) data of the past ten years, and deeply analyzes the traffic congestion situation of the next twelve months. The results show that this model can capture the seasonal trend of traffic congestion well, and provide strong decision support for traffic management departments. Through the interpretation and analysis of the forecast, this study gives a series of traffic management suggestions for different months to achieve the alleviation and optimization of urban traffic congestion. In conclusion, this study provides a useful reference for urban traffic management and planning and contributes to the construction of a more efficient, convenient, and sustainable urban transportation system.

Identifying Traffic Congestion Patterns of Urban Road Network Based on Traffic Performance Index

Urban congestion has become a global problem with urbanization and motorization. The analysis of time-varying traffic congestion patterns is necessary to formulate effective management strategies. The existing studies have focused on traffic flow patterns developed by the volume, speed and density of road sections in a limited district, while the long-time analysis of congestion patterns of the macro road network at the city level is inadequate. This paper aims to recognize traffic congestion patterns of the urban road network based on the traffic performance index (TPI) of 699 days in 2018, 2019 and 2021 in Beijing. The self-organizing maps (SOM) method improved by an automatic clustering number determination algorithm is proposed to cluster congestion patterns based on time-varying TPI. The traffic congestion of the macro road network is clustered into Mondays, Fridays, ordinary weekdays, congested weekdays, weekdays of winter and summer vacation, Saturdays, Sundays and festivals patterns. Patterns of Mondays and congested weekdays have a prominent morning peak, while patterns of Fridays, ordinary weekdays, and weekdays of winter and summer vacation have a prominent evening peak. Saturdays, Sundays and festivals are less congested than weekday patterns. It is verified that the SOM method proposed in this paper clusters traffic congestion into more detailed and accurate patterns, and it is applicable to TPI clustering in different years. The degree of congestion in 2021 increases by 7.15% in peak hours and decreases by 7.50% in off-peak hours compared with that in 2019 due to COVID-19. This method is helpful for traffic management in terms of making decisions according to different congestion patterns.

Research on Cold Chain Routing Optimization of Multi-distribution Center Considering Traffic Performance Index

In response to the practical situation where traffic congestion factors reduce the distribution efficiency and increase the cost of cold chain logistics in the actual transportation process, a mathematical model for optimizing the cold chain path of multiple distribution centers is established, with the goal of minimizing the comprehensive cost and incorporating a traffic congestion index. By analyzing routine factors such as customer requested time windows, loading and unloading time, and road congestion, an improved particle swarm algorithm that introduces exchange offspring is proposed. The proposed method is tested through experimental simulations and the results are compared with traditional methods. The results show that considering the traffic congestion index saves 7.12% of the comprehensive cost compared to not considering the traffic congestion index, and completes the delivery service with a shorter path length.

Identification of Recurrent Congestion in Main Trunk Road Based on Grid and Analysis on Influencing Factors

To reduce the risk of traffic congestion to residents and the urban transportation system, this paper extracted frequently congested areas and major trunk roads based on the GPS (global positioning system) data of cabs and TPI (traffic performance index) data and identified traffic patterns and main trunk roads in the traffic grid, so as to analyze the evolution of traffic congestion and make effective suggestions. The results can not only enable travelers to effectively avoid peak periods and congested sections but also support the managers to optimize urban planning and implement efficient traffic management methods. The research process of this study is as follows: firstly, the research object area was divided into different grids based on one-week taxi GPS data and the distribution characteristics of taxi operations in Qingdao. Secondly, the two-dimensional grid traffic attribute information is constructed using the following two indicators: number of vehicles and the average speed of passenger trajectory. Then, the congestion discriminant model based on the three-dimensional traffic attribute information was established according to the variation rules of the number of position point in the grid. Finally, the TPI data was applied to compare and evaluate the identification results of the above two models to identify frequently congested grids and main trunk roads. The case analysis showed that the result of grid’s congestion status identification considering three-dimensional traffic attribute information (25.198%) was better than that of grid congestion state considering two-dimensional traffic attribute information (23.997%).

Exploring the spatiotemporal pattern of traffic congestion performance of large cities in China: A real-time data based investigation

The socioeconomic costs of traffic congestion are crippling in urbanising China. This study is a first attempt to explore the spatiotemporal pattern of traffic congestion performance in 77 Chinese large cities by using real-time big data. Based upon the hourly real-time traffic performance index data collected between August 27, 2019 to September 27, 2019, four clusters of cities have been captured in line with their urban traffic congestion pattern between different days of the week. Empirical results unveil that urban traffic congestion performance varies substantially between surveyed cities on the same day of the week. Cities with advanced planning and delivery of urban road network, and well-developed urban public transportation system exhibit better traffic performance. Cities with relatively smaller scale of urban population, higher per capita road area and less amount of vehicles have also achieved relatively smooth traffic congestion performance. Particularly, cities in Northeast China tend to have earlier morning and evening peak hours than other sample cities, and the Northeastern cities are overwhelmingly diagnosed with more severe traffic congestion. Moreover, the traffic congestion patterns of surveyed large cities in China present obvious variations between different days of the week. This study provides valuable lens to understand the variegated pattern of traffic congestion performance between different regions in urban China, based upon which targeted policy recommendations have been synthesised to help alleviate urban traffic congestion and further improve urban well-being across the country.

Evaluation of geosynthetic reinforcement in unpaved road using moving wheel load test

The common cause of failure of the unpaved road is associated with undesirable ruts and deformations. Use of geosynthetic reinforcement is a solution to this pavement distress problem as experienced in limited research works, especially in the laboratory studies. This study presents the performance of geosynthetic-reinforced unpaved roads subjected to moving wheel load tests to investigate the effect of geosynthetic reinforcement on the pavement surface deformation of the unpaved roads. Unreinforced and geosynthetic-reinforced unpaved road test sections consisting of varied reinforcements were constructed in a test pit, 9 m long and 2.7 m wide. Geogrid and geotextile were used for reinforcing the unpaved road test sections. The rut depth was measured in the transverse direction of the wheel path after certain number of wheel passes. Traffic Benefit Ratio (TBR) and Performance Index (PI) were employed in the study for the evaluation of the effectiveness of geosynthetic reinforcement in unpaved roads. After 350 vehicle passes, the geotextile-reinforced and geogrid-reinforced test sections get rutting reduced by 44.89% and 28.57%, respectively. The test results indicate that inclusion of geosynthetic reinforcement significantly improves the rutting resistance and stability of reinforced test sections compared to the unreinforced test sections.

A Dynamic Bayesian Network-Based Real-Time Crash Prediction Model for Urban Elevated Expressway

Traffic crash is a complex phenomenon that involves coupling interdependency among multiple influencing factors. Considering that interdependency is critical for predicting crash risk accurately and contributes to revealing the underlying mechanism of crash occurrence as well, the present study attempts to build a Real-Time Crash Prediction Model (RTCPM) for urban elevated expressway accounting for the dynamicity and coupling interdependency among traffic flow characteristics before crash occurrence and identify the most probable risk propagation path and the most significant contributors to crash risk. In this study, Dynamic Bayesian Network (DBN) was the framework of the RTCPM. Random Forest (RF) method was employed to identify the most important variables, which were used to build DBN-based RTCPMs. The PC algorithm combined with expert experience was further applied to investigate the coupling interdependency among traffic flow characteristics in the DBN model. A comparative analysis among the improved DBN-based RTCPM considering the interdependency, the original DBN-based RTCPM without considering the interdependency, and Multilayer Perceptron (MLP) was conducted. Besides, the sensitivity and strength of influences analyses were utilized to identify the most probable risk propagation path and the most significant contributors to crash risk. The results showed that the improved DBN-based RTCPM had better prediction performance than the original DBN-based RTCPM and the MLP based RTCPM. The most probable risk influencing path was identified as follows: speed on current segment (V) (time slice 2)⟶V (time slice 1)⟶speed on upstream segment (U_V) (time slice 1)⟶Traffic Performance Index (TPI) (time slice 1)⟶crash risk on current segment. The most sensitive contributor to crash risk in this path was V (time slice 2), followed by TPI (time slice 1), V (time slice 1), and U_V (time slice 1). These results indicate that the improved DBN-based RTCPM has the potential to predict crashes in real time for urban elevated expressway. Besides, it contributes to revealing the underlying mechanism of crash and formulating the real-time risk control measures.

Design of a Support System for Complicated Logistics Location Integrating Big Data

Logistics location is an important component of logistics planning that affects traffic pressure and vehicle emissions. To date, there has not been an adequate study of the integration of big data into the location for a complicated logistics system. This study developed a decision support system that can address location problems for complicated logistics systems, e.g., a multilevel urban underground logistics system (ULS), using logistics big data. First, information needed in the logistics location, such as the traffic performance index (TPI) and the origin/destination (OD) matrix, was collected and calculated using a big data platform, and this information was digitized and represented based on a geographic information system (GIS) tool. Second, a two‐stage location model for a ULS was designed to balance the construction costs and traffic congestion. The first stage is establishing a set‐covering model to identify optimum locations for secondary hubs based on the ant colony optimization algorithm, and the second stage is clustering of the secondary hubs to determine locations for primary hubs using the iterative self‐organizing data analysis technique algorithm (ISODATA). Finally, the Xianlin district of Nanjing, China, was chosen as a case study to validate the effectiveness of the proposed system. The system can be used to facilitate logistics network planning and to promote the application of big data in logistics.

Spatial–temporal differences in operational performance of urban trunk roads based on TPI data: The case of Qingdao

The purpose of this study is to classify and analyze the traffic performance characteristics of urban trunk roads. It can help travelers effectively avoid peak hours and crowded routes, and traffic managers formulate some refined management and control on roads. This paper extracts the operating characteristics of the trunk roads on macro level by analyzing the Traffic Performance Index (TPI) data. The trunk roads are divided into three types using the agglomerative hierarchical clustering algorithm. According to the research of the spatial–temporal principles and differences of various types in traffic performance, the links with frequent traffic jams are identified, and the mechanism of the different traffic conditions is illustrated. It can be concluded that the trunk road is good conditions in most cases. On weekdays, there are two obvious peak times every day. On weekends, TPI data shows that there is no obvious peak time, and the overall road traffic conditions on weekends are better than those on weekdays. In addition, there are significant differences in the change trajectory of the TPI data and spatial distribution of various types of trunk roads. The roads with frequent traffic jams mainly come from the first and third type.

Research on traffic congestion characteristics of city business circles based on TPI data: The case of Qingdao, China

This study aims to investigate the congestion of the urban business circle based on traffic performance index (TPI) data. A hierarchical clustering algorithm is adopted for the data analysis. A dataset of nearly 64,260 pieces of TPI data from July to October 2017 in Qingdao is collected, and its features, such as interval characteristics, mean time distribution and spatiotemporal correlation, are analyzed. The results show that the southern coastal commercial circle of Qingdao is more congested than the other circles; there exists a morning and afternoon peak, with two peaks on workdays; otherwise, weekend and vacation periods do not show congestion. The congestion level toward the end of the vacation week (October 4th–8th) is lower than that during the beginning (October 1st–3rd). Considering the temporal and spatial dimensions, the causes of the two different congestion states during the holidays are speculated upon The characteristics of traffic congestion in Qingdao business circle and its possible causes are proposed, and a new design and overall plan for Qingdao is promoted.

Design of a heuristic environment-friendly road pricing scheme for traffic emission control under uncertainty

Road transportation is one of the main sources of atmospheric emissions in many countries and areas. Road pricing, is not only effective for urban transportation management, but also helpful in reducing the negative externalities caused by transportation. In this study, an inexact two-phase minimal emission programming (TMEP) model is proposed for design of the environment-friendly toll scheme with an acceptable road network performance. Through introduction of fuzzy stochastic programming, multiple uncertainties involved in vehicle emission evaluation are dealt with; the Traffic Performance Index (TPI) based constraints are incorporated to reflect the decision-maker's requirements for network congestion management. The solution method is proposed for generating the range of fuzzy stochastic objectives. An optimal toll scheme associated with the minimal emission based flow pattern is obtained through searching for a set of the best and the worst optimal solutions. A numerical experiment and a real-world road network in Beijing of China are used to illustrate the application of the developed method. In the case study, the toll scheme is obtained at the desired congestion level. The effects of emission and congestion abatement are analyzed under different policy scenarios. The proposed TMEP method can generate the toll scheme with obvious improvements in total emission reduction and congestion mitigation.

Cordon- or Link-Based Pricing: Environment-Oriented Toll Design Models Development and Application

This study focuses on an environment-friendly toll design problem, where an acceptable road network performance is promised. First, a Traffic Performance Index (TPI)-based evaluation method is developed to help identify the optimal congestion level and the management target of a transportation system. Second, environment-oriented cordon- and link-based road toll design models are respectively proposed through the use of bi-level programming. Both upper-level submodel objectives are to minimize gross revenue (the total collected toll minus the emissions treatment cost) under different pricing strategies. Both lower-level submodels quantify the user equilibrium (UE) condition under elastic demand. Moreover, the TPI-related constraints for the management requirements of the network performance are incorporated into the bi-level programming modeling framework, which can lead to 0–1 mixed integer bi-level nonlinear programming for toll design problems. Accordingly, a genetic algorithm-based heuristic searching method is proposed for the two pricing models. The proposed cordon- and link-based pricing models were then applied to a real-world road network in Beijing, China. The effects of the toll schemes generated from the two models were compared in terms of emissions reduction and congestion mitigation. In this study, it was indicated that a higher total collected toll may lead to more emissions and related treatment costs. Tradeoffs existed between the toll scheme, emissions reduction, and congestion mitigation.

Time-Space Relationship Analysis Model on the Bus Driving Characteristics of Different Drivers Based on the Traffic Performance Index System

Time-Space Relationship Analysis Model on the Bus Driving Characteristics of Different Drivers Based on the Traffic Performance Index System

多源数据融合的交通指数标准化云平台技术

多源数据融合的交通指数标准化云平台技术

Analysis of rainfall effects on road travel speed in Beijing, China

Although rainfall affects travel speed on different road classes to different degrees, few existing studies focus on the impacts of rainfall on different urban road classes. Utilising real-time weather data, floating car data-based travel speed on urban roads and traffic performance index in Beijing, this study analyses and compares the differences between travel speeds for normal versus rainfall weather under different precipitation intensities, different congestion levels and different road classes. The study demonstrates that when precipitation intensity reaches the heavy rain level, travel speeds on the expressway, major arterial and collector decrease by 7.5, 5.0 and 9.4%, respectively, at night, and decrease by 15.2, 13.4 and 12.2%, respectively, during peak hours. Further, this study derives a relationship between precipitation intensity and travel speed reduction for different congestion levels. The light-congestion is the most sensitive traffic condition to rainfall when there is moderate or heavier rain. Finally, a correction model for travel speed predictions for rainfall weather is developed and applied. It is indicated that the model can predict travel speed under rainfall weather effectively.

A spatial-temporal estimation model of residual energy for pure electric buses based on traffic performance index

A spatial-temporal estimation model of residual energy for pure electric buses based on traffic performance index

Study on Extract and Repair Traffic Performance Index Data of Beijing

Urban traffic expert developed many methods to judge the performance of urban road to provide driving instruction to both drivers and the local authority. In Beijing, the traffic performance index is widely used by people every day. In order to store the traffic performance index data which is continually changed to local database, our team has tried many different ways to achieve it because we want to do deep analyze of it. By using VBA programming we successfully build a data extract system to store data. We use two different ways to process the data from the system based on data missing type. At last, the data is ready for further study to find the traffic pattern of urban area.

Study on Traffic Congestion Patterns of Large City in China Taking Beijing as an Example

Urban traffic congestion has different typical characteristics under the influence of different conditions, such as different day of week, holiday and weather etc. It is necessary to set up the relationships between traffic congestion patterns and those influencing factors, when we conducting macroscopic analysis on the causes of traffic congestion. Based on Traffic Performance Index (TPI), a dynamic macroscopic index showing the whole area congestion intensity developed in 2007, typical congestion patterns are identified by using clustering method. A comparative analysis is conducted on setting rules for different clustering indexes. TPI pattern curves are derived and verified under combinations of date, transportation demand management policy, holiday, weather condition and etc., according to the actual traffic operational status. The analysis and verification results show that the method used in this paper is effective and feasible. TPI patterns indicate that traffic congestion has inherent characteristics which are primary and essential for transportation managers. This paper lays the foundation for traffic congestion prediction and early warning and proactive alleviation of traffic congestions.