This research was aimed at developing an area occupancy–based method for estimating passenger car unit (PCU) values for vehicle categories under heterogeneous traffic conditions on multilane urban roads for a wide range of traffic flow levels. First, PCU values of vehicle categories were determined according to the Transport and Road Research Laboratory definition and replaced the commonly considered measure of performance speed with area occupancy using simulation. The PCU values obtained were found to be significantly different for different volume-to-capacity ratios; this result shows that the PCU value is dynamic in nature. While the dynamic nature of PCU values is well appreciated, practitioners may prefer a single set of optimized PCU values (unique for each vehicle category). Hence, a new method with a matrix solution was proposed to estimate the optimized or unique set of PCU values with area occupancy as the performance measure. To check the credibility of the proposed method, the estimated PCU values were compared from existing guidelines regulated by the Indian Roads Congress (IRC) and values estimated with the widely accepted dynamic PCU concept of speed–area ratio. Results show that the PCU values suggested by IRC and the dynamic PCU concept using the speed–area ratio underestimate and overestimate the flows, respectively, at different traffic volumes. However, the values obtained with the area-occupancy concept were found to be consistent with the traffic flow in a cars-only traffic situation at different flow conditions. The derived set of optimized PCU values proposed can be useful for traffic engineers, researchers, and practitioners for capacity and level-of-service analysis under heterogeneous traffic conditions.
This research focused on analyzing the association between transit service reliability indicators and ridership. Further, the effect of road network, demographic, socioeconomic, and land use characteristics on transit service reliability was analyzed. The analysis was conducted at a bus stop level. Bus arrival/departure and ridership data from the Charlotte Area Transit System (CATS) was obtained. The road network, demographic, socioeconomic, and land use characteristics were captured within 0.25-mile and 0.50-mile buffers. Pearson correlation analysis was conducted to understand the association between road network, demographic, socioeconomic, and land use characteristics and bus transit service reliability measures. The results show that bus transit service reliability has a substantial impact on ridership and is influenced by road network, demographic, socioeconomic, and land use characteristics within the bus stop vicinity. The findings help public transportation agencies to effectively utilize available resources, plan, and provide equitable services to all riders.
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