Real-time traffic information, such as travel time or its inverse—journey speed—is critical to the successful implementation of advanced traveler information systems. Yet it is often costly to measure travel time or journey speed directly. Reliable methods have been developed to estimate travel time and journey speed for freeways. Estimation models for arterial travel time, however, are lacking. A simple model for estimating travel speed over an arterial link is presented. The model combines two speed estimates—one from critical volume to capacity ratio and the other from volume and occupancy measured by loop detectors—to produce more accurate estimated link journey speeds that can be used to provide real-time assessment of traffic congestion levels on arterial streets. A preliminary evaluation of the proposed model is also provided, and the model is compared with two other arterial travel time models. Limitations of the model are explained, and areas for potential improvement are suggested.

Link-Journey-Speed Model for Arterial Traffic:

Travel time estimation and prediction on urban arterials is an important component of Active Traffic and Demand Management Systems (ATDMS). This paper aims in using the information of GPS probes to augment less dynamic but available information describing arterial travel times. The direction followed in this paper chooses a cooperative approach in travel time estimation using static information describing arterial geometry and signal timing, semi-dynamic information of historical travel time distributions per time of day, and utilizes GPS probe information to augment and improve the latter. First, arterial travel times are classified by identifying different travel time states, then link travel time distributions are approximated using mixtures of normal distributions. If prior travel time data is available, travel time distributions can be estimated empirically. Otherwise, travel time distribution can be estimated based on signal timing and arterial geometry. Real-time GPS travel time data is then used to identify the current traffic condition based on Bayes Theorem. Moreover, these GPS data can also be used to update the parameters of the travel time distributions using a Bayesian update. The iterative update process makes the posterior distributions more and more accurate. Finally, two comprehensive case studies using the NGSIM Peachtree Street dataset, and GPS data of Washington Avenue in Minneapolis, were conducted. The first case study estimated prior travel time distributions based on signal timing and arterial geometry under different traffic conditions. Travel time data were classified and corresponding distributions were updated. In addition, results from the Bayesian update and EM algorithm were compared. The second case study first tested the methodologies based on real GPS data and showed the importance of sample size. In addition, a methodology was proposed to distinguish new traffic conditions in the second case study.

Probe vehicle based real-time traffic monitoring on urban roadways

Vehicle travel time on arterial roads is a crucial parameter for traffic management and traveler information systems. A travel time distribution is an effective way to represent the essential properties of this parameter. This paper proposes a modified Gaussian Mixture Model for representing travel time distributions on arterial roads with signalized intersections. The proposed model is applicable to travel time data from both fixed and mobile sensors. The performance of the model was evaluated using real travel time measurements from fixed sensors in the field and virtual mobile sensor data generated from those real-world measurements. The evaluation results show very good performance of the model in representing the traffic state on arterial roads. The model can be applied to historical datasets to estimate the amount of stop time and non-stop time for vehicles on arterial links during a specific time period, which is useful information for a variety of traffic applications, such as arterial travel time prediction and arterial traffic energy/emission estimation.

Arterial roadway travel time distribution estimation and vehicle movement classification using a modified Gaussian Mixture Model

Traffic congestion is a common phenomenon in our daily lives that greatly costs society. A better understanding of the interaction between freeways and arterial streets may help traffic engineers and researchers improve the operation of existing facilities and deploy feasible traffic diversion plans to improve the usage of existing road capacity within a traffic network. This paper proposes a novel two-step approach to evaluate the interaction between freeways and arterial streets by comparing their performances. The first step is to identify freeway and arterial travel time pattern similarities via template matching techniques commonly used in computer vision. The interaction is quantified in the second step by using conditional probability theory. The result of the two-step process allows analysts to determine whether traffic diversion is possible or likely between freeways and parallel arterials. The city of Bellevue, Washington was selected as a case study site because of the availability of traffic s...

Evaluation of Arterial and Freeway Interaction for Determining the Feasibility of Traffic Diversion

Modelling & monitoring the performance of urban traffic control systems

The envisioned operational tests of Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS) in the Minneapolis/St. Paul area call for the provision of timely and reliable travel times over an entire road network. Travel time can be directly measured by certain new detection technologies, such as Automatic Vehicle Identification (AVI) or Automatic Vehicle Location (AVL) systems, however, these new technologies are not widely deployed and are much more costly than loop detectors. Finding an accurate way to estimate link travel time using loop detector data offers great economic benefits. This project examines the development of improved arterial travel time models. In the project's first phase, researchers reviewed existing travel time models, collected traffic data, and developed a travel time database. The project's second phase will seek to develop and evaluate new travel time estimation models.

https://ir.uiowa.edu/cgi/viewcontent.cgi?article=1006&context=ppc_transportation

Michael Zhang

Papers

Arterial Link Travel Time Estimation Using Loop Detector Data

Arterial link travel time estimation using loop detector data - phase i