Abstract
Traffic sensors serve as an important way to a number of intelligent transportation system applications which rely heavily on real-time data. However, traffic sensors are costly. Therefore, it is necessary to optimize sensor placement to maximize various benefits. Arterial street traffic is highly dynamic and the movement of vehicles is disturbed by signals and irregular vehicle maneuver. It is challenging to estimate the arterial street travel time with limited sensors. In order to solve the problem, the paper presents travel time estimation models that rely on speed data collected by sensor. The relationship between sensor position and vehicle trajectory in single link is investigated. A sensor location model in signalized arterial is proposed to find the optimal sensor placement with the minimum estimation error of arterial travel time. Numerical experiments are conducted in 3 conditions: synchronized traffic signals, green wave traffic signals, and vehicle-actuated signals. The results indicate that the sensors should not be placed in vehicle queuing area. Intersection stop line is an ideal sensor position. There is not any fixed sensor position that can cope with all traffic conditions.
Highlights
Traffic sensors are widely used in transportation system for systematic surveillance
These three models all use the speed measured by sensors, so the travel time estimation error is caused by inevitable calculation error, and the error arising from the location of sensor in arterial street
If the arrival rate is greater than the intersection capacity, the first intersection will have a small number of queuing vehicles; this is, because some vehicles enter the arterial street at a smaller speed, their travel time on the link is greater than the travel time of vehicle which moves at the limited speed
Summary
Traffic sensors (e.g., magnetic detectors, cameras, and bluetooth detectors) are widely used in transportation system for systematic surveillance. Li et al [7] studied that most of the travel time estimation methods or algorithms require many sources of real time data Investment in these transportation surveillance devices is costly, and providing these pieces of real time information is expensive. Sensor location problem works for this purpose It aims to find an optimal sensor placement pattern either in transportation network or freeway. Due to the complexity of urban transportation system, none of current studies consider combining travel time estimation method with a sensor location pattern to seek an optimal sensor placement pattern. Another important characteristic of urban transportation system is traffic signal control.
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