Cross-cutting Issues

Wireless communication between vehicles and the transportation infrastructure will provide

significantly more timely and comprehensive information about arterials and their performance.

However, most measures-of-effectiveness were developed based on data available from

traditional “point” sensors. The information made available in a connected vehicle environment

requires new metrics that can fully utilize the data. This paper identifies several new arterial

performance metrics made available in a connected vehicle environment, as well as several

existing metrics that can be evaluated more accurately and frequently than before. The new

metrics are person-delay, sudden deceleration, change in lateral acceleration, and aggregate

regulation compliance. Person-delay measures a vehicle’s lost time multiplied by the number of

passengers, and allows for more efficient movement of high-occupancy vehicles and

sophisticated transit signal priority. Sudden deceleration and change in lateral acceleration

measure activities such as unexpected braking and swerving, which may be leading indicators of

unsafe conditions. Aggregate regulation compliance detects unsafe driving behavior that is

difficult to collect in the field, such as speeding and illegal U-turns. Engineers can address

problem areas through signal timing changes traffic calming, and other measures. The proposed

metrics all require high-resolution detection, and are difficult or impossible to measure with

existing point detection. For each new metric, its compatibility with connected vehicles is

discussed, and required SAE J2735 DSRC Message Set Dictionary data elements are identified.

Virginia Center for Transportation Innovation and Research, Virginia DOT

University of Virginia

Presented at the 18th World Congress on ITS, October 2011, Orlando, Florida

The primary objective of this paper is to study and analyze the temporal characteristics of

traffic congestions and the associated speed distributions for congested expressways based on

the Floating Car Data (FCD) in Beijing. First, the temporal distributions of congestions with

different intensities are analyzed using the FCD of 5 workdays for a particular expressway

Link N26376. Results show that the longer the continuous congestion lasts, the lower the

speed distribution interval is. Then, the distribution curve is fitted, which finds that the

Log-Logistic model can well describe the survival function distribution of the severe

congestion durations for morning peak-hours of Tuesday. By comparison, it is found that the

duration distribution value of severe congestions at the aggregation interval of 2-minute is

lower than that at the 5-minute interval, and the duration distribution value of severe

congestions on Link N26376 is higher than that on its downstream Links N24162 and N23971.

Finally, the speed distributions in two proposed categories of congestion periods are analyzed

and compared. Results show that if the speed in the present time period falls in a higher

distribution interval, then the probability that the severe congestion will dissipate in the next

time period will be higher.

School of Traffic and Transportation, Beijing Jiaotong University

Texas Southern University

Beijing Transportation Research Center

Presented at the 18th World Congress on ITS, October 2011, Orlando, Florida