Evaluating light rail sketch planning: actual versus predicted station boardings in Phoenix

In recent years, transit planners are increasingly turning to simpler, faster, and more spatially detailed “sketch planning” or “direct demand” models for forecasting rail transit boardings. Planners use these models for preliminary review of corridors and analysis of station-area effects, instead of or prior to four-step regional travel demand models. This paper uses a sketch-planning model based on a multiple regression originally fitted to light-rail ridership data for 268 stations in nine U.S. cities, and applies it predictively to the Phoenix, Arizona light-rail starter line that opened in December, 2008. The independent variables in the regression model include station-specific trip generation and intermodal–access variables as well as system-wide variables measuring network structure, climate, and metropolitan-area factors. Here we compare the predictions we made before and after construction began to pre-construction Valley Metro Rail predictions and to the actual boardings data for the system’s first 6 months of operations. Depending on the assumed number of bus lines at each station, the predicted total weekday ridership ranged from 24,767 to 37,907 compared with the average of 33,698 for the first 6 months, while the correlation of predicted and observed station boardings ranged from r = 0.33 to 0.47. Sports venues, universities, end-of-line stations, and the number of bus lines serving each station appear to account for the major over- and under-predictions at the station level.     

Simulation assessment of incident detection by cellular phone call-in programs

This research study was designed to assess by simulation the efficacy of incident detection by cellular phone call-in programs. The assessment was conducted by varying the proportion of drivers with cellular phones on the highway so as to mirror the cellular industry statistics that show a continued growth of ownership of cellular phones in the United States. An analytical model, which combined simulation and the limited field data available in the literature, was used to determine measures of effectiveness of the cellular phone-based detection system. The results showed that a cellular phone detection system offers fast incident detection times and higher detection rates for both shoulder and lane blocking incidents. For example, in moderate traffic flow (i.e. 1,550 vehicles per hour per lane), 90 percent of incidents blocking two lanes were detected in 1.5 minutes when the proportion of drivers with cellular phones was one out of 10 drivers, even with only 20 percent of them willing to report incidents. When the current proportion of cellular ownership, i.e. 1 out of 3, was used in the simulation, the detection time improved to 0.8 minutes. The simulation analysis of incident detection by cellular phones also showed that there is a direct relationship between the probability of detection and the detection time; that is, the specification of a higher detection rate resulted in slower detection times. This is in sharp contrast with the results of field study of automatic incident detection (AID) systems which demonstrated an inverse relationship between probability of detection and detection time. This revised version was published online in June 2006 with corrections to the Cover Date.     

Simulator evaluation of incident detection by transponder-equipped vehicles

This paper explores the efficacy of the driver-based incident detection using the vehicle-to-roadside communication (VRC) system. The proliferation of vehicle tags in the US for automatic toll collection, traffic monitoring, and vehicle navigation and information systems has created an infrastructure capable of supporting a driver-based incident detection system. The research reported herein investigated the use of "activatable" vehicle tags by drivers to send an incident signal to the Traffic Management Center through VRC reader stations spaced uniformly on a highway. The simulation results showed that good detection performance was achieved even at lower levels of market penetration of vehicle tags. The results further showed that detection performance is significantly affected by the severity of the incident in terms of number of lanes closed, the spacing of the VRC reader stations, traffic volume at the time of the incident, and the reporting propensity of the traveling public.The performance of the VRC-based incident reporting system was compared to the performance of two incident detection algorithms that rely on traffic data collected through the automatic vehicle identification (AVI) system. The comparison showed that the VRC-based incident reporting system attained shorter detection times and higher detection rates under fairly similar simulated conditions. The paper also discusses issues that need further study through simulation and field experimentation of the VRC-based incident reporting system.