Dynamic Traffic Assignment Model Based on GPS Data and Point of Interest (POI) in Shanghai

Dynamic traffic flow, which can facilitate the efficient operation of traffic road networks, is an important prerequisite for the application of reasonable assignment of traffic demands in an urban road network. In order to improve the accuracy of dynamic traffic flow assignment, this paper proposes a dynamic traffic flow assignment model based on GPS trajectory data and the influence of POI. First, this paper explores the impact patterns of POI on regional road network congestion during peak hours through qualitative and quantitative analysis. Then, based on the user equilibrium theory, a dynamic traffic flow assignment model, in which the effect of POI on links is reflected using the link-node impedance function, is proposed. Finally, the accuracy of the model is validated by the GPS trajectory data and origin–destination (OD) traffic data of motor vehicles in Xuhui District, Shanghai, China. The results show that the model can be used to coordinate and optimize the traffic assignment of the regional road network under the influence of POI during peak hours and alleviate the congestion of the road network. The findings can provide a powerful reference for developing scientific and rational traffic assignment decisions and management strategies for urban road network traffic.

Analysis of Spatial-Temporal Characteristics of Operations in Public Transport Networks Based on Multisource Data

Operational efficiency and stability are two critical aspects to measure bus systems. Influenced by many stochastic factors, buses always suffer from delay and bunching. Traditional studies focus on a single route and lack research on the systematic evaluation of bus network. In this paper, we propose a data-driven framework to analyze the efficiency and stability based on small granularity GPS trajectory data from the perspective of entire bus network. The IC card data and route data are used to extract the boarding passenger number and topological structure, respectively. The results show that the average headway of stations follows a lognormal distribution. Moreover, the distribution of arrival efficiency of stations is inhomogeneous and a small number of stations have large values. In addition, the relationships among average headway of stations, boarding passenger number, bus number, and complex network indicators are revealed. It is found that the average headway of station is negatively correlated with other indicators, which implies that complex network connections and more passenger flows could weaken the efficiency of bus operations. This paper provides a way to evaluate the operational performance of bus networks and could give help for monitoring and optimizing the daily operation of bus systems.

Recognition of Operating Characteristics of Heavy Trucks Based on the Identification of GPS Trajectory Stay Points

As an emerging data source, feature identification based on GPS trajectory data has become a hot issue in the field of data mining and freight management. A method of trajectory data extraction, classification, and visualization based on stay points was proposed in this paper to analyze the operation characteristics of heavy trucks from the perspectives of intracity transportation, intraprovincial transportation, and interprovincial transportation. The GPS trajectory data of heavy trucks in Sichuan Province in March 2019 were taken as an example to analyze the operation characteristics. The results show that the heavy trucks in Sichuan Province are mainly transported within the province, and the freight efficiency is slightly better than the average level of the national freight trucks in the same period, failing to give full play to the advantages of long transport distance. The manufacturing industry is the main service object of heavy trucks, and the vehicles engaged in transportation within the province are more dependent on logistics enterprises and their ancillary facilities. The north-south longitudinal line and east-west horizontal line are the main interprovincial transport channels, and the provincial and municipal transport is mainly concentrated in some urban trunk lines, ring lines, and express routes. The proposed technical method can describe the operating characteristics of freight trucks from the perspective of microscopic and service market, not only to guide the layout of highway freight yards, logistics parks, and logistics hubs and the determination of service functions but also to provide a reference basis for freight management-related departments and drivers to formulate transportation plans and establish freight information platforms to improve freight efficiency and safety.

Identifying the Key Nodes and Sections of Urban Roadway Network Based on GPS Trajectory Data

This paper proposes a novel approach to identify the key nodes and sections of the roadway network. The taxi-GPS trajectory data are regarded as mobile sensor to probe a large scale of urban traffic flows in real time. First, the urban primary roadway network model and dual roadway network model are developed, respectively, based on the weighted complex network. Second, an evaluation system of the key nodes and sections is developed from the aspects of dynamic traffic attributes and static topology. At the end, the taxi-GPS data collected in Xicheng District of Beijing, China, are analyzed. A comprehensive analysis of the spatial-temporal changes of the key nodes and sections is performed. Moreover, the repetition rate is used to evaluate the performance of the identification algorithm of key nodes and sections. The results show that the proposed method realizes the expression of topological structure and dynamic traffic attributes of the roadway network simultaneously, which is more practicable and effective in a large scale.

Automatically Tracking Road Centerlines from Low-Frequency GPS Trajectory Data

High-quality digital road maps are essential prerequisites of location-based services and smart city applications. The massive and accessible GPS trajectory data generated by mobile GPS devices provide a new means through which to generate maps. However, due to the low sampling rate and multi-level disparity problems, automatically generating road maps is challenging and the generated maps cannot yet meet commercial requirements. In this paper, we present a GPS trajectory data-based road tracking algorithm, including an active contour-based road centerline refinement algorithm as the necessary post-processing. First, the low-frequency trajectory data were transferred into a density estimation map representing the roads through a kernel density estimator, for a seeding algorithm to automatically generate the initial points of the road-tracking algorithm. Then, we present a template-matching-based road-direction extraction algorithm for the road trackers to conduct simple correction, based on local density information. Last, we present an active contour-based road centerline refinement algorithm, considering both the geometric information of roads and density information. The generated road map was quantitatively evaluated using maps offered by the OpenStreetMap. Compared to other methods, our approach could produce a higher quality map with fewer zig-zag roads, and therefore more accurately represents reality.

Developing a Regional Drive Cycle Using GPS-Based Trajectory Data from Rideshare Passenger Cars: A Case of Chengdu, China

A drive cycle describes the microscopic and macroscopic vehicle activity information that is crucial for emission quantification research, e.g., emission modeling or emission testing. Well-developed drive cycles capture the driving patterns representing the traffic conditions of the study area, which usually are employed as the input of the emission models. By considering the potential of large-scale GPS trajectory data collected by ubiquitous on-vehicle tracking equipment, the objective of this study is to demonstrate the capability of GPS-based trajectory data from rideshare passenger cars for urban drive cycle development. Large-scale GPS trajectory data and order data collected by an app-based transportation vehicle was used in this study. GPS data were filtered by thresholds of instantaneous accelerations and vehicle specific powers. The micro-trip selection-to-rebuild method with operating mode distribution was used to develop a series of speed-bin categorized representative drive cycles. Sensitivity of the time-of-day and day-of-week were analyzed on the developed drive cycles. The representativeness of the developed drive cycles was verified and significant differences exist when they are compared to the default light-duty drive cycles coded in MOVES. The findings of this study can be used for helping drive cycle development and emission modeling, further improving the understanding of localized emission levels.