Salvador Hernandez

A time of day analysis of crashes involving large trucks in urban areas

Previous studies have looked at different factors that contribute to large truck-involved crashes, however a detailed analysis considering the specific effects of time of day is lacking. Using the Crash Records Information System (CRIS) database in Texas, large truck-involved crashes occurring on urban freeways between 2006 and 2010 were separated into five time periods (i.e., early morning, morning, mid-day, afternoon and evening). A series of log likelihood ratio tests were conducted to validate that five separate random parameters logit models by time of day were warranted. The outcomes of each time of day model show major differences in both the combination of variables included in each model and the magnitude of impact of those variables. These differences show that the different time periods do in fact have different contributing factors to each injury severity further highlighting the importance of examining crashes based on time of day. Traffic flow, light conditions, surface conditions, time of year and percentage of trucks on the road were found as key differences between the time periods.

Large Truck–Involved Crashes: Exploratory Injury Severity Analysis

In recent years, a growing concern related to large-truck accidents has increased due to potential economic impacts and level of injury severity that can be sustained. Yet, studies related to large-truck involved crashes are scarce and lack human behavior factors that can greatly influence crash outcomes. In this study, the authors present an analysis of data from the fusion of several national data sets addressing large-truck involved injury severity. This is done by considering human, road-environment, and vehicular factors in large-truck involved crashes on U.S. interstates. A random parameter ordered probit model was estimated to predict the likelihood of five injury severity outcomes—fatal, incapacitating, non-incapacitating, possible injury, and no injury. The modeling approach accounts for possible unobserved effects relating to human, vehicular, and road environment factors not present in the data. Estimation findings indicate that the level of injury severity is highly influenced by a number of complex interactions of factors and that the effect of the some of the factors can vary across the observations.

Modeling injury outcomes of crashes involving heavy vehicles on Texas highways

Concern related to crashes that involve large trucks has increased in Texas recently because of the potential economic impacts and level of injury severity that can be sustained. However, detailed studies on large truck crashes that highlight the contributing factors leading to injury severity have not been conducted in Texas, especially for its Interstate system. The contributing factors related to injury severity were analyzed with Texas crash data based on a discrete outcome-based model that accounts for possible unobserved heterogeneity related to human, vehicle, and road–environment factors. A random parameter logit (i.e., mixed logit) model was estimated to predict the likelihood of five standard injury severity scales commonly used in the Crash Records Information System in Texas: fatal, incapacitating, nonincapacitating, possible, and none (i.e., property damage only). Estimation results indicated that the level of injury severity outcomes was highly influenced by several complex interactions between factors and that the effects of some factors could vary across observations. The contributing factors include driver demographics, traffic flow, roadway geometric features, land use, time characteristics, weather, and lighting conditions.

Centralized Carrier Collaboration Multihub Location Problem for Less-Than-Truckload Industry: Hybrid Hub-and-Spoke Network

A centralized carrier collaboration multihub location problem (CCCMLP) for the small to medium-sized less-than-truckload industry is addressed. In the CCCMLP, a central entity (e.g., a third-party logistics firm) seeks a set of collaborative consolidation transshipment hubs to establish a hybrid collaborative hub-and-spoke system that minimizes the total collaborative costs for the set of collaborating carriers. Previous studies focused on addressing the exchange of capacity without considering the location of transfer hubs and the routes that connect them. A carrier has the option either to collaborate or to ship its demand directly without collaborating. The decision depends on the expected profit margin over shipping directly while following a revenue-generating, rate-setting behavior. The CCCMLP was formulated as a variant of the P-hub location problem, which is NP-hard and solved with Lagrangian relaxation. Numerical experiments were conducted to gain insight into the performance of the CCCMLP formulation under various network sizes and numbers of hubs. The results indicate that larger expected profit margins from collaborative carriers applying revenue-generating behavior would increase the likelihood of collaboration by carriers. As the network size increases, the effect of hybrid hub location costs drops.

A carrier collaboration problem for less-than-truckload carriers: characteristics and carrier collaboration model

This paper addresses a single-carrier collaboration problem (SCCP) in which a less-than-truckload (LTL) carrier of interest seeks to collaborate with other carriers by acquiring capacity to service excess demand. The SCCP is addressed from a static (planning) perspective to gain insights into the potential of the collaboration concept for carriers, and its ability to alleviate the effects of increased fuel prices. The study also explores the impact of the degree of collaboration represented by the collaborative discount rate on the carrier of interest. The collaborative strategies are compared to the non-collaboration option represented by a short-term leasing strategy, and the relative benefits of collaboration are computed. Single- and multiple-product SCCPs are formulated as binary (0–1) multi-commodity minimum cost flow problems and are solved using the branch-and-cut algorithm. Experiments are conducted for two transfer cost policies to illustrate insights into the computational performance under varying factors, the effects of different degrees of collaboration, and the impacts of energy costs on the potential for collaboration. The results illustrate that a higher degree of collaboration leads to increased benefits for the carrier of interest and reduced dead-heading for the collaborating carriers. Collaboration also can be critical for the survival of the small- to medium-sized LTL carriers as energy prices escalate given the small industry-wide profit margins.

Centralized Time-Dependent Multiple-Carrier Collaboration Problem for Less-Than-Truckload Carriers

This paper addresses a time-dependent, centralized multiple-carrier collaboration problem (TD-MCCP) for the small to medium-sized less-than-truckload (LTL) industry. The TD-MCCP represents a strategy in which a central entity (such as a third-party logistics firm) seeks to minimize the total system costs of an LTL carrier collaborative that consists of multiple carriers by identifying collaborative opportunities over a shared network under three rate-setting behavioral strategies and a leasing alternative. In contrast to conventional time-dependent network problems that view demand as dynamic, capacities in the proposed LTL multiple-carrier collaborative framework are time-dependent but known a priori, and demand is fixed. The TD-MCCP is modeled as a binary (0–1) multi-commodity minimum cost-flow problem formulation for two rate-setting behavioral cases and solved with a branch-and-cut algorithm. The first case examines the effect of one rate-setting behavioral strategy at a time, and the second case examines the effect of multiple rate-setting behavioral strategies simultaneously. Numerical experiments are conducted to seek insights into the computational performance of the TD-MCCP formulations under various network sizes and numbers of shipments. The results indicate that the attractiveness of the time-dependent multiple-carrier collaboration paradigm increases with a volume-oriented rate-setting strategy. Also, a volume-oriented rate strategy has the potential to increase the capacity utilization of carriers seeking to minimize empty-haul trips. Finally, the leasing alternative can serve as a viable option for a centralized collaborative system, especially when affordable collaborative capacity is scarce.

Fatality rates for crashes involving heavy vehicles on highways: A random parameter tobit regression approach

ABSTRACT Few studies have analyzed the impacts of freight movements (large truck) on crash fatality rates. This study explores a novel application of a method, namely the random parameters tobit regression model to large truck fatality rates by using the nationwide Fatality Analysis Reporting System. By utilizing random parameter tobit regression the authors examined crash rates (instead of frequencies) in per million truck-miles traveled and ton-miles of freight in the United States as continuous censored variables. The empirical and statistical results illustrate that the random-parameters tobit regression model provides a better understanding of the fatality rates per million truck-miles traveled and ton-miles of freight over the fixed parameter tobit model. Factors related to the crash mechanism, temporal and spatial characteristics, road and environmental attributes, vehicle configuration, drivers and passenger attributes were found to be statistically significant. Some exposure to injury severity related factors also were found to be significant with random parameters that vary across the observations.

Identifying Precrash Factors for Cars and Trucks on Interstate Highways: Mixed Logit Model Approach

This research investigates the factors that lead to three manners of collision (namely, rear-end, angle, and sideswipe) that occurred in the same direction of multilane interstate highways. Mixed logit (MXL) models were developed to estimate the probability of rear-end, angle, and sideswipe collisions as functions of vehicle-following attributes and other driving maneuvers immediately before collisions. The National Automotive Sampling System-General Estimates System crash data set, collected from 2005 to 2008, was used to estimate the model. This research analyzes collisions among passenger cars and trucks, with an emphasis on their vehicular characteristics. RESULTS show that driving behavior is different when vehicular characteristics are different and when roles of the striking and struck vehicles are grouped according to cars and trucks. This research contributes to a better understanding of the differences in unsafe driving acts between cars and trucks, and implications on future policies on car and truck drivers. Language: en

An empirical analysis of run-off-road injury severity crashes involving large trucks

In recent years, there has been an increasing interest in understanding the contributory factors to run-off-road (ROR) crashes in the US, especially those where large trucks are involved. Although there have been several efforts to understand large-truck crashes, the relationship between crash factors, crash severity, and ROR crashes is not clearly understood. The intent of this research is to develop statistical models that provide additional insight into the effects that various contributory factors related to the person (driver), vehicle, crash, roadway, and environment have on ROR injury severity. An ordered random parameter probit was estimated to predict the likelihood of three injury severity categories using Oregon crash data: severe, minor, and no injury. The modeling approach accounts for unobserved heterogeneity (i.e., unobserved factors). The results showed that five parameter estimates were found to be random and normally distributed, and varied across ROR crash observations. These were factors related to crashes that occurred between January and April, raised median type, loss of control of a vehicle, the indicator variable of speed not involved, and the indicator variable of two vehicles or more involved in the crashes. In contrast, eight variables were found to be fixed across ROR observations. Looking more closely at the fixed parameter results, large-truck drivers who are not licensed in Oregon have a higher probability of experiencing no injury ROR crash outcomes, and human related factor, fatigue, increases the probability of minor injury. The modeling framework presented in this work offers a flexible methodology to analyze ROR crashes involving large trucks while accounting for unobserved heterogeneity. This information can aid safety planners and the trucking industry in identifying appropriate countermeasures to help mitigate the number and severity of large-truck ROR crashes.

An Approach to Comprehensively Evaluate Potential Park and Ride Facilities

ABSTRACT A park and ride facility provides an option to car drivers to park their cars and switch to public transportation for the remaining portions of their trips. Although park and ride has been implemented in many cities in the United States and integrated with different modes of transportation, no comprehensive approach has been developed in published literature to assess the feasibility of a potential park and ride site. This research proposes a comprehensive approach, which consists of the following tasks, to evaluate potential park and ride facilities: 1 Site location analysis 2 Bus system reliability analysis 3 Parking supply and usage analysis 4 Mode choice model 5 User demand and ridership estimation 6 Cost estimation and economic impacts analysis The application of the proposed tasks was demonstrated through a case study of a site in the City of El Paso, Texas.