The significant impact of heavy vehicles (HVs) on freeway operations has been identified since the first edition of the Highway Capacity Manual (HCM). The HCM 2010 used passenger car equivalent (PCE) values and percentages of trucks, buses, and recreational vehicles to account for the effect of HVs on roadway performance. Unfortunately, the PCE values in the HCM 2010 relied on a limited field database and simulation runs that were calibrated for steady-flow traffic conditions, although the effect of HVs on traffic flow could reasonably have been expected to have varied with traffic conditions. Few studies have been conducted with extensive field data to examine impacts on traffic characteristics by HV presence under congested and forced-flow conditions. This paper presents such an effort by using urban freeway data containing 1.2 million individual vehicle observations. The results indicated a significant difference in lagging–leading behavior between vehicle pairs related to HV presence. Passenger car and HV headways were found to increase with HV presence in the traffic stream. A similar pattern was found for the PCE factor. The PCE value under congested conditions and more than 9% HV presence was found to be 1.76, which was higher than the 1.5 value recommended by the HCM 2010 for level freeway sections. The maximum throughput of the freeway was found to be affected by HV presence. The maximum throughput was observed at a truck presence of 3%, after which it became progressively lower. The results of this paper can be used as input for future simulation runs of congested freeway flow conditions.
For the past few years, several studies have focused on identifying a vehicle’s trajectory with smartphone data. However, these studies predominantly used GPS coordinate information for that purpose. Considering the known limitations of GPS, such as connectivity issues at urban canyons and underpasses, low precision of localization, and high power consumption of smartphones while GPS is in use, this paper focuses on developing alternative methods for identifying a vehicle’s trajectory at an intersection with sensor data other than GPS to minimize GPS dependency. In particular, accelerometer and gyroscope data collected with smartphone inertial sensors and speed data collected with an onboard diagnostics device are used to develop algorithms for maneuver (i.e., left and right turns and through) and trip direction identification at an intersection. In addition, techniques for noise removal and orientation correction from raw inertial sensor data are described. The effectiveness of the method for trajectory identification is assessed with collected field data. Results demonstrate that the developed method is effective in identifying a vehicle’s trajectory at an intersection. Overall, this research shows the feasibility of using alternative sensor data for trajectory identification and thus eliminating the need for continuous GPS connectivity.
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