Phani Kumar Patnala

This article investigates the quality of axle load data collected from a portable weigh-in-motion (PWIM) system installed near a permanent WIM in Manitoba, Canada. Using the permanent WIM as the reference dataset, the investigation examined record pairing, calibration drift, the impact of temperature and speed on data quality, per-vehicle data validity, and aggregated data validity. The findings showed that the PWIM system was unable to produce sufficiently accurate data for most applications. This was true even when the system was precalibrated and subject to temperature corrections and autocalibration postprocessing. Error tolerances two to three times higher than those specified by ASTM (Type II) would be required to meet the standard. The results corroborated previous findings in the literature and reinforced the skepticism practitioners have about PWIM data validity. However, the investigation offered practical lessons concerning PWIM system deployment and postprocessing that could support further research and development. Specifically, PWIMs exhibit limited sensitivity to highway speeds and may serve as a cost-effective alternative for collecting traffic and axle load data on secondary highways. The postprocessing methods were found to be effective at stabilizing measurements for fully loaded vehicles. Further, the Gaussian mixture modeling technique proposed in this article offers an approach for using PWIM data to attribute higher quality load data to the PWIM site.

This study attempts to develop (i) truck safety performance functions (SPFs), and (ii) hazard-specific crash modification factors (CMFs), for cold-region rural highways. Police-reported truck-involved crashes on rural highway segments of Alberta, Canada, were used to develop truck SPFs for four crash severity levels: total, fatal, personal injury (PI), and property damage only. Three settings of the Poisson–Tweedie regression modeling approach representing Poisson, geometric Poisson, negative binomial distributions were used to develop truck SPFs; the negative binomial distribution was deemed as the most appropriate distribution to model truck-involved crashes for all crash severity levels. The CMF for poor visibility (CMF = 1.5) suggests that poor visibility increases PI-type truck-involved crashes on rural two-lane two-way highway segments by 50% as compared to the number of such crashes attributed to crash causes other than transportation hazards. Road safety researchers may adopt the methodology to effectively rank hazard risks to highway freight transportation systems.

The present study demonstrated the potential applications of reliability models for crash analysis of a large highway network. Specifically, three major outcomes of reliability models were investigated: temporal distributions of crashes, reliability score, and expected number of crashes, using 20-year data (2001–2020) of crashes recorded on the Saskatchewan highway network. A series of reliability models were developed for crashes by crash severity, vehicle involvement, and highway type. First, the temporal distributions of crashes on each segment were fit to a statistical distribution. Second, the reliability scores were used to rank the high crash risk segments. Third, the mean expected crash frequency was used to develop network-wide safety performance functions for total and fatal crashes in urban and rural highway segments using Poisson–Tweedie (PTw) regression models. The developed PTw models showed that the presence of trucks in the traffic composition has a significant effect on crash frequency, especially for urban highway segments.

Resilience is a multidisciplinary concept that deals with rapid response and recovery of a system experiencing a disruption. Despite extensive research on this topic, there is a need to clarify resilience concepts in the context of road freight transportation systems (FTSs) from a three-dimensional perspective (i.e., physical infrastructure, users, and managing organizations) and to identify persistent knowledge gaps concerning the characterization and measurement of FTS resilience vis-à-vis disruptive events. This paper addresses these shortcomings through a systematic review of 149 research studies. The synthesis of findings clarifies inconsistencies associated with the characteristics of FTS resilience and in so doing, establishes a unified framework for measuring FTS resilience through the life cycle of disruptive events. Critical data gaps, methodological shortcomings, and a lack of empirical evidence concerning FTS resilience to disruptive events remain. More robust analytical approaches are required to incorporate interdependencies among FTS dimensions into resilience assessments.