Modeling Highway Traffic Safety for Developing Countries Using Delphi Technique and Bayesian Network： Case Study for Nigeria

Highway traffic safety is an issue confronting developing countries and those of industrialized nations. Nigeria, as a developing country, has been experiencing unusually high traffic related injury and fatality rates. In order to commence combating highway safety problems in Nigeria, the first task is to identify the major contributing factors; however, Nigeria has no reliable and comprehensive database of traffic accidents and casualties. Consequently, the Delphi technique was utilized in generating the required data such as number of registered automobiles, number of licensed drivers, and annual fatality count for modeling and forecasting accident rates in Nigeria. A Bayesian network model was developed and used, with the data obtained from Delphi process, to demonstrate possible traffic safety responses to different scenarios of changes in the Nigerian socio-political culture. Although the Delphi technique and the Bayesian network model only estimate the accident and safety data, those methods can be a realistic option when those data are not available, especially for the developing countries. As a result, the major accident contributors have been identified and the top three contributors-road condition, DUI （driving under the influence） and reckless driving-are policy related. The Nigerian traffic safety outlook would improve significantly if the existing laws and policies can be enforced, even at a very moderate level.

Evaluation and Effectiveness of Reallocating Traffic Signal Timing at Corridors with High Freight Volume

The COVID-19 pandemic drastically altered traffic patterns across the globe.With more people staying at home and relying on delivery services,there was a reduction in passenger car traffic but an increase in freight traffic.This study investigates the changes in traffic volume and mode splits during the COVID-19 pandemic at the intersection of Broening Highway and Holabird Avenues and investigates traffic signal reallocation strategies that account for the changes in traffic.This study considers morning peak traffic volumes before and during COVID.A methodology was developed to evaluate and measure freight volumes and emissions.From the literature,the cost per kilogram of CO,VOC,and NOx was$5.85,$3,37,and$12.53,respectively.The intersection operated at a level of service(LOS)of C both pre-COVID and during COVID.Traffic volume decreased by 24.3%during COVID at the study location;car volumes declined by 50%,and truck volumes increased by 130%.The total rate of emissions decreased by 22.6%.

Influence of work zone signage on driver speeding behavior

Speeding in a work zone needs to be tackled effectively to enhance work zone safety. This study evaluates the influence of three different work zone signs—speed photo enforced signs, dynamic speed display signs and reduced speed limit signs—on driver speeding behavior using a medium-fidelity driving simulator. The speed photo enforced and reduced speed limit signs were placed at intervals before the start of the work zone and in the work zone. A virtual work zone network was built to replicate the Baltimore–Washington Parkway(MD-295). A total of 66 participants from different socioeconomic backgrounds participated in 264 driving simulation sessions. The speeds were measured over four phases: initial speed area, sign visible area, sign readable area and post sign area. An analysis of variance and post hoc analysis showed that the speed photo enforced sign was the most effective of the three signs. The second speed photo enforced sign within the work zone was more effective at reducing speed than the sign before the start of the work zone, especially in the post sign area. It was observed that female participants tend to slow down more when encountering a speed photo enforcement sign.

Traffic recovery time estimation under different flow regimes in traffic simulation

Incident occurrence and recovery are critical to the smooth and efficient operations of freeways. Although many studies have been performed on incident detection, clearance, and management, travelers and traffic managers are unable to accurately predict the length of time required for full traffic recovery after an incident occurs. This is because there are no practical studies available to estimate post-incident recovery time. This paper estimates post-incident traffic recovery time along an urban freeway using traffic simulation and compares the simulation results with shockwave theory calculations. The simulation model is calibrated and validated using a freeway segment in Baltimore, MD. The model explores different flow regimes （traffic intensity） and incident duration for different incident severity, and their effects on recovery time. A total of 726 simulations are completed using VISSIM software. Finally, the impact of congestion and incident delay on the highway network is quantified by a regression formula to predict traffic recovery time. The developed regression model predicts post-incident traffic recovery time based on traffic intensity, incident duration, and incident severity （ratio of lanes closure）. In addition, three regression models are developed for different flow regimes of near-capacity, moderate, and low-traffic intensity. The model is validated by collected field data on two different urban freeways.