The traffic volume and roadway capacity are the important basic inputs required for planning, analysis, and operation of roadway systems. Expressing traffic volume as number of vehicles passing a given section of road or traffic lane per unit time will be inappropriate when several types of vehicles with widely varying static and dynamic characteristics are comprised in the traffic. The problem of measuring volume of such heterogeneous traffic has been addressed by converting different types of vehicles into equivalent passenger cars and expressing the volume in terms of passenger car unit (PCU) per hour. The vehicles of highly heterogeneous traffic such as the one prevailing on Indian roads may occupy any convenient lateral position on the road, based on the availability of space, without any lane discipline. This study is concerned with the estimation of PCU values of vehicles in such traffic conditions, using microscopic simulation. The PCU values obtained for different types of vehicles, for a wide ra...

Predicting bus arrival times and travel times are crucial elements to make the public transport more attractive and reliable. The present study explores the use of Intelligent Transportation Systems (ITS) to make public transportation systems more attractive by providing timely and accurate travel time information of transit vehicles. However, for such systems to be successful, the prediction should be accurate, which ultimately depends on the prediction method as well as the input data used. In the present study, to identify significant inputs, a data mining technique, namely k-NN classifying algorithm is used. It is based on the similarity in pattern between the input and historic data. These identified inputs are then used for predicting the travel time using a model-based recursive estimation scheme, based on Kalman filtering. The performance is evaluated and compared with methods based on static inputs, to highlight the improved prediction accuracy.

The Bureau of Public Roads (BPR) function is a widely used link cost function in transportation planning because of its simple mathematical form, easily measurable field variables, and consistent performance. However, the BPR function is deterministic and does not capture the stochastic relation between travel time and traffic flow. The present study develops a modified BPR (MBPR) function by incorporating travel time uncertainty (TTU) in the deterministic BPR function. In the MBPR function, the effect of TTU is incorporated using two parameters, [ ] and [ ]. A nonlinear optimization problem is formulated, and the generalized reduced gradient method is used to calibrate the BPR and the MBPR function. The applicability of the proposed MBPR function is demonstrated using empirical data collected for an urban arterial in India and simulated data developed for a real-world urban road network. The proposed MBPR function captures the heterogeneity in travel time for different traffic flow values. The function (a) captures the variability in travel time under oversaturated conditions and (b) captures the time-dependent relation between traffic volume and delay. The physical meaning of [ ] and [ ] in the context of inter-day heterogeneity, infrastructure potential, and traffic flow heterogeneity are discussed. The practical application of MBPR as an analytical tool for system-wide performance evaluation is demonstrated by investigating the impact of traffic signal control on travel times using a before–after perspective. Compared with the before case, a 13% reduction in travel time is observed for the after case. Therefore, the installation of traffic signal control has reduced congestion.

The research work reported here investigates driving behavior under mixed traffic conditions on high-speed, multilane highways. With the involvement of multiple vehicle classes, high-resolution trajectory data is necessary for exploring vehicle-following, lateral movement, and seeping behavior under varying traffic flow states. An access-controlled, mid-block road section was selected for video data collection under varying traffic flow conditions. Using a semi-automated image processing tool, vehicular trajectory data was developed for three different traffic states. Micro-level behavior such as lateral placement of vehicles as a function of speed, instant responses, vehicle-following behavior, and hysteresis phenomenon were evaluated under different traffic flow states. It was found that lane-wise behavior degraded with increase in traffic volume and vehicles showed a propensity to move towards the median at low flow and towards the curb-side at moderate and heavy flows. Further, vehicle-following behavior was also investigated and it was found that with increase in flow level, vehicles are more inclined to mimic the leader vehicle’s behavior. In addition to following time, perceiving time of subject vehicle for different leading vehicles was also evaluated for different vehicle classes. From the analysis, it was inferred that smaller vehicles are switching their leader vehicles more often to escape from delay, resulting in less following and perceiving time and aggressive gap acceptance. The present research work reveals the need for high-quality, micro-level data for calibrating driving behavior models under mixed traffic conditions.