Recent research has investigated various means of measuring link travel times on freeways. This search has been motivated in part by the fact that travel time is considered to be more informative to users than local velocity measurements at a detector station. But direct travel time measurement requires the correlation of vehicle observations at multiple locations, which in turn requires new communications infrastructure and/or new detector hardware. This paper presents a method for estimating link travel time using data from an individual dual loop detector, without requiring any new hardware. The estimation technique exploits basic traffic flow theory to extrapolate local conditions to an extended link. In the process of estimating travel times, the algorithm also estimates vehicle trajectories. The work demonstrates that the travel time estimates are very good provided there are no sources of delay, such as an incident, within a link.

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Estimating travel times and vehicle trajectories on freeways using dual loop detectors

Due to the increase in vehicle transit and congestion in road networks, providing information about the state of the traffic to commuters has become a critical issue for Advanced Traveller Information Systems. These systems should assist users in making pre-trip and en-route decisions and, for this purpose, delivering travel time information is very useful because it is very intuitive and easily understood by all travellers. The aim of this paper is to present a global view of the literature on the modelling of travel time, introducing essential concepts and giving a thorough classification of the existing techniques. Most of the attention will focus on travel time estimation and travel time prediction, which are two of the most relevant challenges in travel time modelling. The definition and goals of these two modelling tasks along with the methodologies used to carry them out will be further explored and categorised.

A review of travel time estimation and forecasting for Advanced Traveller Information Systems

This report describes the prototype design, development, analysis and performance of a traffic surveillance system that is based on wireless sensor networks. Vehicle classification and reidentification schemes for low-cost, low-power platforms with limited computation resources were developed and tested. Both acoustic and magnetic sensors were tested. A robust real-time vehicle detection algorithm for both sensors was developed, and the magnetic sensor was shown to be superior to the acoustic sensor. The detection accuracy was shown to be comparable to that of inductive loop detectors while also having a much higher configuration flexibility, thus allowing the wireless sensor network system to be scalable and deployable everywhere in the traffic networks.

Traffic Surveillance by Wireless Sensor Networks: Final Report

A practical system is described for the real-time estimation of travel time across an arterial segment with multiple intersections. The system relies on matching vehicle signatures from wireless sensors. The sensors provide a noisy magnetic signature of a vehicle and the precise time when it crosses the sensors. A match (re-identification) of signatures at two locations gives the corresponding travel time of the vehicle. The travel times for all matched vehicles yield the travel time distribution. Matching results can be processed to provide other important arterial performance measures including capacity, volume/capacity ratio, queue lengths, and number of vehicles in the link. The matching algorithm is based on a statistical model of the signatures. The statistical model itself is estimated from the data, and does not require measurement of ‘ground truth’. The procedure does not require measurements of signal settings; in fact, signal settings can be inferred from the matched vehicle results. The procedure is tested on a 1.5 km (0.9 mile)-long segment of San Pablo Avenue in Albany, CA, under different traffic conditions. The segment is divided into three links: one link spans four intersections, and two links each span one intersection.

Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensors

Smart cities have been a global concern in recent years, involving comprehensive scientific research. To obtain a structural overview and assist researchers in making insights into the characteristics of smart cities research, bibliometric analysis was carried out in this paper. With the application of the bibliometric analysis software VOSviewer and CiteSpace, 4409 smart cities were identified by the core collection of the Web of Science in publications between 1998 and 2019 and used in the analysis of this paper. Concretely, this research visually demonstrates a comprehensive overview of the field relating to smart cities in terms of the production of regular publications, main domain of smart cities researchers, most influential countries (institutions, sources and authors), and interesting research directions in the smart city researches. We also present the research collaboration among countries (regions), organizations and authors based on a series of cooperation analyses. The bibliometric analysis of the existing work provided a valuable and seminal reference for researchers and practitioners in smart cities-related research communities.

https://www.mdpi.com/2071-1050/11/13/3606/pdf

Bibliometric Analysis on Smart Cities Research

The vehicle reidentification problem is the task of matching a vehicle detected at one location with the same vehicle detected at another location from a feasible set of candidate vehicles detected at the other location. This paper formulates and solves the vehicle reidentification problem as a lexicographic optimization problem. Lexicographic optimization is a preemptive multi-objective formulation, and this lexicographic optimization formulation combines lexicographic goal programming, classification, and Bayesian analysis techniques. The solution of the vehicle reidentification problem has the potential to yield reliable section measures such as travel times and densities, and enables the measurement of partial dynamic origin/destination demands. Implementation of this approach using conventional surveillance infrastructure permits the development of new algorithms for ATMIS (Advanced Transportation Management and Information Systems). Freeway inductive loop data from SR-24 in Lafayette, California, demonstrates that robust results can be obtained under different traffic flow conditions.

Use of vehicle signature analysis and lexicographic optimization for vehicle reidentification on freeways

Despite the advent of new detection systems such as video, infrared, microwave, and ultrasound, inductive loop detectors (ILD) still remain the most widely used sensors for traffic information. For example, the California Department of Transportation alone has approximately 300,000 ILD installations, and that number excludes the installations that are part of local cities and agencies. By using these ILDs in "smarter" ways, useful section-related traffic system parameters can be derived, including densities and travel times. Consequently, the existing detection infrastructure can be used for application in many areas of intelligent transportation systems and, especially, in real-time dynamic systems. The process of developing feature extraction and vehicle pattern matching algorithms and the subsequent derivation of section-related measures based on conventional inductive loops are discussed. Field data collected from State Route 24 in Lafayette, Calif., were used for algorithm development and for the tes...

Kevin Tsai

Papers

Use of vehicle signature analysis and lexicographic optimization for vehicle reidentification on freeways

Algorithm development for derivation of section-related measures of traffic system performance using inductive loop detectors