Vehicle Traffic Characteristics Research Articles

Developing a Highway Rail Grade Crossing Accident Probability Prediction Model: A North Dakota Case Study

Safety at highway rail grade crossings (HRCs) continues to be a serious concern despite improved safety practices. Accident frequencies remain high despite increasing emphasis on HRCs safety. Consequently, there is a need to re-examine both the design practices and the safety evaluation methods at HRCs. Previous studies developed accident prediction models by incorporating highway, crossing inventory, rail, and vehicle traffic characteristics, but none of these factors considered population in the vicinity of HRCs. This study developed a binary logit regression model to predict accident likelihood at HRCs by incorporating various contributory factors in addition to population (based on census blocks 2010) within five miles of crossings. Previous North Dakota accident data from 2000 to 2016 was analyzed and used in the model development. The model results show that the number of daily trains, the maximum typical train speed, the number of through railroad tracks, and the number of highway/traffic lanes all affect accident likelihood. The presence of pavement markings in the form of stop lines helps reduce accident probability, while populations within five miles of HRCs have a positive relationship with crash likelihood. This study will help transportation agencies improve HRC safety.

Study on Composition and Simulation Analysis of Traffic Loads in Submerged Floating Tunnels

For Submerged Floating Tunnels (SFTs) under operation, traffic load is a variable fundamental load and an important part of various loads they bear. The traffic loads of SFTs involve many factors such as vehicle parameters, vehicle speeds, road conditions, underwater conditions, etc. Based on the vehicle traffic characteristics and the structural properties of SFTs and with a comprehensive consideration of the influence mechanism of traffic loads, this paper puts forward an expression of traffic loads under the action of wave forces through an analysis of the composition and influence factors of traffic loads in SFTs. Furthermore, it analyzes the influence degrees and application range of such traffic loads by means of a numerical analysis, which may serve as a reference for related research on dynamic response of SFTs.

Particulate dust emission factors from unpaved roads in the U.S.–Mexico border semi-arid region

Unpaved road dust emissions in El Paso in Texas, Las Cruces in New Mexico and Ciudad Juárez in Mexico were measured using the Testing Re-entrained Aerosol Kinetic Emissions from Roads (TRAKER) system. PM10 (particles with diameter < 10 μm) emissions factors from unpaved roads in Ciudad Juárez, Mexico increased by a factor of five as compared to those measured in El Paso, TX and Las Cruces, NM. The highest emission factors were observed in spring. A strong exponential dependence of PM10 emissions factors on vehicle speed was observed in Ciudad Juárez, Mexico and El Paso, TX, whereas they remained constant in Las Cruces, NM reflecting differences in soil surface characteristics. The highest PM10 emissions in Las Cruces and Ciudad Juárez originated from the sideline tire, indicating the possible influence of accumulated loose soil and debris on the side of the unpaved roads. Overall, strong spatial and temporal variability of PM10 emission factors were computed in the Paso del Norte region, reflecting differences in road surface and vehicle traffic characteristics and further underscored the need for high spatiotemporal resolution of emission inventories to accurately identify the most susceptible unpaved roads and control their burden on particulate pollution.

Analysis of Electric Vehicle Traffic Characteristics Considering Recharging Behavior

Analysis of Electric Vehicle Traffic Characteristics Considering Recharging Behavior

Cyclist safety on bicycle boulevards and parallel arterial routes in Berkeley, California

This study compares the safety of bicyclists riding on bicycle boulevards to those riding on parallel arterial routes in Berkeley, California. Literature on the impact of motor vehicle traffic characteristics on cyclist safety shows that high motor vehicle speeds and volumes and the presence of heavy vehicles are all detrimental to cyclist safety. This suggests that cyclists may be safer on side streets than on busy arterials. Bicycle boulevards—traffic-calmed side streets signed and improved for cyclist use—purport to offer cyclists a safer alternative to riding on arterials. Police-reported bicycle collision data and manually collected cyclist count data from bicycle boulevards and parallel arterial routes in Berkeley, California from 2003 to 2010 are used to test the hypothesis that Berkeley's bicycle boulevards have lower cyclist collision rates and a lower proportion of bicycle collisions resulting in severe injury. While no significant difference is found in the proportion of collisions that are severe, results show that collision rates on Berkeley's bicycle boulevards are two to eight times lower than those on parallel, adjacent arterial routes. The difference in collision rate is highly statistically significant, unlikely to be caused by any bias in the collision and count data, and cannot be easily explained away by self-selection or safety in numbers. Though the used dataset is limited and the study design is correlational, this study provides some evidence that Berkeley's bicycle boulevards are safer for cyclists than its parallel arterial routes. The results may be suggestive that, more generally, properly implemented bicycle boulevards can provide cyclists with a safer alternative to riding on arterials.

Spatial Propagation of Information in Vehicular Networks

There has been increasing interest in building an information infrastructure for mobile vehicles in surface transportation systems that principally rely on vehicle-to-vehicle (v2v) communications. The various network architectures proposed for this purpose have unique characteristics that distinguish them from other systems. However, only a limited amount of work has been completed to understand the fundamental network properties of such systems. In this paper, we present analytical models to study the spatial propagation of information in a highly mobile v2v ad hoc network. We explore both one- and two-way vehicle traffic scenarios. These models can help better understand data dissemination in this environment. Our results show that information propagation depends on some vehicle traffic characteristics, e.g., vehicle density, average vehicle speed, and relative vehicle movement. These models lead to some interesting discoveries, e.g., a message can propagate in the opposite direction as the vehicle traffic flow and can propagate much faster than vehicle movement.