Using registration plate analysis, this paper investigates 1) the proportions of ‘unique’ vehicles, 2) the proportions of vehicles re-appearing from day-to-day and their individual arrival variances and 3) the numbers of locally registered vehicles among those seen during the morning commute period on three roads in Southampton. During incidents, a traffic controller would hope to divert the more familiar drivers onto less congested parts of the network using VMS and other media. Knowing the proportions of regular and unique drivers which make up the peak commuting periods would help in the timely dissemination of this traffic information.
The proportions of unique vehicles varied significantly with road and time. Vehicles appearing on more than one day formed 80% of the traffic before 08:15 but only 60% between the 08:45 and 09:00 peak period during the 1996 Bassett Avenue survey (F(4,20) = 60.1, p The results suggest that for occasions where congestion can be anticipated in advance, such as prior to emergency roadworks or special events, warning messages would be most effective before 08:30 a.m. when the largest proportion of regular vehicles would be using the roads. If regular users are more familiar with the local road network than one-off ‘unique’ vehicles, and would be more likely to divert on receiving incident information relevant to their route, then later in the morning, the proportion of knowledgeable local drivers falls substantially.

Traffic Composition during the Morning Peak Period. Implications for Urban Traffic Management Systems

Fast processors offer exciting opportunities for real-time traffic monitoring. Conventional transportation planning models that assume stable and predictable travel patterns do not lend themselves to on-line traffic forecasting. This paper describes how a new traffic flow inference model has the potential to determine comprehensive flow information in real time. Its philosophical basis is borrowed from the field of operational research, where it has been used for optimizing water and electricity flows. This paper shows how road traffic turning movement flows can be estimated from link detected flows at small recurrent intervals, in real time. The paper details the formulation of the problem, outlines the structure of the data set that provides the detector data for the model input and observed turning flows for the model evaluation. The theoretical principles that define the model are described briefly. Turning movement flow estimates, at 5-minute intervals, from two independent surveys are presented and analyzed. The results show an overall mean coefficient of determination of 79-82% between observed and modeled turning movement flows.

Turning Movement Estimation in Real Time

Origin-Destination (OD) studies are often used in transportation planning to determine the travel patterns (origin-destination matrix) of vehicles and goods in a particular area. Given these travel patterns, the impacts of alternative solutions to current and future transportation problems can be evaluated. Therefore, it is important that the travel patterns be accurately measured. However, it is not always clear what data collection method should be used to obtain the type of data needed, while maximizing quality and minimizing the time and cost. The objective of this research is to review both conventional and experimental techniques for roadside station OD studies, and make general recommendations for the best OD study technique and data collection method, given the roadway characteristics and traffic conditions.

/pdf/guidelines-for-data-collection-techniques-and-methods-for-1w9ly21cil.pdf

Guidelines for Data Collection Techniques and Methods for Roadside Station Origin-Destination Studies

In this project, a prototype knowledge-based expert system (KBES) integrated with a Geographic Information System (GIS) is developed. Geomedia Pro is used as the GIS interface for data entry. A software bridge is also implemented to ensure swift data exchange between Synchro, which is a commercial signal optimization software package used by the New Jersey Department of Transportation (NJDOT), and the developed prototype. This integration will allow the users to transfer intersection data between Synchro and the prototype KBES tool in an efficient way. The rule base of the prototype KBES was developed using the information that exists in the literature, including surveys conducted by other researchers and the simulation studies conducted by the Rutgers research team. The performance of three distinct adaptive control strategies were assessed by using a macroscopic and a microscopic simulation tool namely, PARAMICS. Prototypes for reactive (SCOOT-like), case-based/reactive (SCATS-like) and proactive/predictive (OPAC-like algorithms), each using a different control logic, were developed. These prototypes were tested for various well-calibrated New Jersey intersections. The outcome of these simulation studies was then used to develop general rules in terms of the effectiveness of using adaptive control strategies under various network and traffic conditions. The developed rule base was implemented in Visual Basic and integrated with the developed prototype. The prototype also has the capability of performing interactive macroscopic simulation of OPAC-like, SCOOT-like, and SCATS-like control strategies given the intersection and traffic characteristics. This feature was added to the KBES system to enable the user to further analyze each individual intersection. Finally, a benefit-cost analysis function is implemented and integrated to further support the decision making process. In short, the developed tool provides the NJDOT traffic engineers and decision makers with a user-friendly suite of tools to guide them in identifying the most suitable intersections for adaptive control and in accurately assessing the potential benefits over existing control.

Evaluation of Adaptive Control Strategies for NJ Highways

Using registration plate analysis, this paper investigates 1) the proportions of ‘unique’ vehicles, 2) the proportions of vehicles re-appearing from day-to-day and their individual arrival variances and 3) the numbers of locally registered vehicles among those seen during the morning commute period on three roads in Southampton. During incidents, a traffic controller would hope to divert the more familiar drivers onto less congested parts of the network using VMS and other media. Knowing the proportions of regular and unique drivers which make up the peak commuting periods would help in the timely dissemination of this traffic information. The proportions of unique vehicles varied significantly with road and time. Vehicles appearing on more than one day formed 80% of the traffic before 08:15 but only 60% between the 08:45 and 09:00 peak period during the 1996 Bassett Avenue survey. Although the proportions of vehicles re-appearing from day to day varied significantly with road, their arrival variances were found to be very similar. On average, 65% of the returning vehicles re-appeared within +/- 5 minutes of their previous day’s time implying that this frequency of arrival could be part of an habitual behaviour pattern. The results suggest that for occasions where congestion can be anticipated in advance, such as prior to emergency roadworks or special events, warning messages would be most effective before 08:30 a.m. when the largest proportion of regular vehicles would be using the roads. If regular users are more familiar with the local road network than one-off ‘unique’ vehicles, and would be more likely to divert on receiving incident information relevant to their route, then later in the morning, the proportion of knowledgeable local drivers falls substantially. (Some mathematical expressions are not shown, please refer to the paper)

https://ojs-lib2-test.tudelft.nl/ejtir/article/download/3675/3792

Traffic Composition during the Morning Peak Period

This paper is the first in a series of three that describe the survey design and the computer methods that were used in measuring turning movement from traffic detector flows, monitored across a signal- controlled network in the City of Leicester, England. Leicester's traffic network is a series of connected radial and ring roads; in 1988, its fixed-time signal control system was replaced by the demand-responsive traffic signal control system SCOOT. The SCOOT Region 'R' was chosen for the study, because its spatial geometry offers alternative routes to traffic entering and leaving the city centre. Numberplate matching was adopted, because it was decided that it was not possible to track all vehicles accurately enough using roadside interviews, travel diaries, video recording, or time-lapse photography. The paper describes a new way of observing registration plates. One challenge that was met was the training of 50 people to use the method, and ensuring that they were sent to the right places at the right time. Each observer was given a training session of one hour. Reports from supervisors and random checks on the reliability of the data transcription both verified that information transfer was accurate. The use of a wide range of recording equipment was investigated, including portable data-loggers and hand-held computers.

Vehicle tracking through unsampled registration-plate observation

The paper begins by describing the role traffic detection systems play in enabling an expert system to define remedial control strategies for congestion management. It highlights the need for algorithms to infer the traffic conditions and route patterns in a city network and justifies the need to study the origin and destinations of all vehicles entering, leaving and travelling within subarea of a SCOOT system, whilst simultaneously monitoring traffic with SCOOT detectors. Finally, the design and results of a number plate survey is described.<
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Peter T. Martin

Papers

Vehicle tracking through unsampled registration-plate observation

Use of detector data for traffic control strategies