A Macro-Micro Approach to Reconstructing Vehicle Trajectories on Multi-Lane Freeways with Lane Changing

Vehicle trajectories can offer the most precise and detailed depiction of traffic flow and serve as a critical component in traffic management and control applications. Various technologies have been applied to reconstruct vehicle trajectories from sparse fixed and mobile detection data. However, existing methods predominantly concentrate on single-lane scenarios and neglect lane-changing (LC) behaviors that occur across multiple lanes, which limit their applicability in practical traffic systems. To address this research gap, we propose a macro-micro approach for reconstructing complete vehicle trajectories on multi-lane freeways, wherein the macro traffic state information and micro driving models are integrated to overcome the restrictions imposed by lane boundary. Particularly, the macroscopic velocity contour maps are established for each lane to regulate the movement of vehicle platoons, meanwhile the velocity difference between adjacent lanes provide valuable criteria for guiding LC behaviors. Simultaneously, the car-following models are extended from micro perspective to supply lane-based candidate trajectories and define the plausible range for LC positions. Later, a two-stage trajectory fusion algorithm is proposed to jointly infer both the car-following and LC behaviors, in which the optimal LC positions is identified and candidate trajectories are adjusted according to their weights. The proposed framework was evaluated using NGSIM dataset, and the results indicated a remarkable enhancement in both the accuracy and smoothness of reconstructed trajectories, with performance indicators reduced by over 30% compared to two representative reconstruction methods. Furthermore, the reconstruction process effectively reproduced LC behaviors across contiguous lanes, adding to the framework's comprehensiveness and realism

Reconstructing the Traffic State by Fusion of Heterogeneous Data

We present an advanced interpolation method for estimating smooth spatiotemporal profiles for local highway traffic variables such as flow, speed and density. The method is based on stationary detector data as typically collected by traffic control centres, and may be augmented by floating car data or other traffic information. The resulting profiles display transitions between free and congested traffic in great detail, as well as fine structures such as stop-and-go waves. We establish the accuracy and robustness of the method and demonstrate three potential applications: 1. compensation for gaps in data caused by detector failure; 2. separation of noise from dynamic traffic information; and 3. the fusion of floating car data with stationary detector data.Comment: For more information see http://www.mtreiber.de or http://www.akesting.d

Dynamic OD matrix estimation exploiting bluetooth data in urban networkss

Time-Dependent Origin-Destination (OD) matrices are a key input to Dynamic Traffic Models. Microscopic and Mesoscopic traffic simulators are relevant examples of such models, traditionally used to assist in the design and evaluation of Traffic Management and Information Systems (ATMS/ATIS). Dynamic traffic models can also be used to support real-time traffic management decisions. The typical approaches to time-dependent OD estimation have been based either on Kalman-Filtering or on bi-level mathematical programming approaches that can be considered in most cases as ad hoc heuristics. The advent of the new Information and Communication Technologies (ICT) makes available new types of traffic data with higher quality and accuracy, allowing new modeling hypotheses which lead to more computationally efficient algorithms. This paper presents a Kalman Filtering approach, that explicitly exploit traffic data available from Bluetooth sensors, and reports computational experiments for networks and corridors.Peer ReviewedPostprint (published version

A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting

With the enrichment of perception method, modern transportation system has many physical objects whose states are influenced by many information factors so that it is a typical Cyber-Physical System (CPS). Thus, the traffic information is generally multi-sourced, heterogeneous and hierarchical. Existing research results show that the multi-sourced traffic information through accurately classifying in the process of information fusion can achieve better parameters forecasting performance. For solving the problem of traffic information accurately classification, via analyzing the characteristics of the multi-sourced traffic information and using redefined binary tree to overcome the shortcomings of the original SVM (Support Vector Machine) classification in information fusion, a multi-classification method using improved SVM in information fusion for traffic parameters forecasting is proposed. The experiment was conducted to examine the performance of the proposed scheme and the results reveal that the method can get more accurate and practical outcomes.  </p

Heterogeneous urban traffic data and their integration through kernel-based interpolation

This paper presents collection and analysis of heterogeneous urban traffic data, and integration of them through a kernel-based approach. The recent development in sensing and information technology opens up opportunities for researching the use of this vast amount of new urban traffic data. In this paper, the data fusion algorithm is developed by using a kernel based interpolation approach. Our objective is to reconstruct the underlying urban traffic pattern with fine spatial and temporal granularity through processing and integrating data from different sources. The fusion algorithm can work with data collected in different space time resolution, with different level of accuracy, and from different kinds of sensors. The properties and performance of the fusion algorithm is evaluated by using a virtual test-bed produced by VISSIM microscopic simulation. The methodology is demonstrated through a real-world application in Central London. This paper contributes to analysis and management of urban transport facilities

Review of data fusion methods for real-time and multi-sensor traffic flow analysis

Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends

A Kalman filter approach for the estimation of time dependent OD matrices exploiting bluetooth traffic data collection

Time-Dependent Origin-Destination (OD) matrices are a key input to Dynamic Traffic Models, microscopic and mesoscopic traffic simulators are relevant examples of such models, traditionally used to assist in the design and evaluation of Traffic Management and Information Systems (ATMS/ATIS). Dynamic traffic models can also be used to support real-time traffic management decisions. The typical approaches to the time-dependent OD estimation have been based either on ad hoc heuristics using mathematical programming approaches, or on Kalman-Filtering. The advent of the new Information and Communication Technologies (ICT), makes available new types of traffic data of higher quality and accuracy allowing for new modeling hypothesis leading to more computationally efficient algorithms. Ad hoc procedures based on Kalman Filtering, explicitly exploiting traffic data available from Bluetooth sensors, have been designed and implemented successfully and the numerical results of the computational experiments are discussed for freeway and network test sites.Peer ReviewedPostprint (published version