Parametric study of stimulus-response behavior incorporating vehicle heterogeneity in car-following models

The objective of this study was to develop a family of car-following models that address the shortcomings of car-following models developed by General Motors (GM) in the 1950s. The developed models consist of separate models for acceleration, deceleration, and steady-state responses for congested freeway traffic conditions. The study calibrated the models using individual vehicle trajectory data collected on a segment of Interstate 101 in Los Angeles, California. Furthermore, the study validated the models using individual vehicle trajectory data collected on a segment of Interstate 80 in Emeryville, California. The study used nonlinear regression with robust standard errors to estimate the model parameters and obtain the distribution of the model parameters across drivers and for different pairs of following vehicles. The stimulus response thresholds that delimit acceleration and deceleration responses were determined based on Signal Detection Theory. The results indicate that average drivers\u27 response time lag is significantly lower for deceleration response than for acceleration response. This is intuitive because deceleration response is generally related to safety, thus, drivers are expected to respond faster than for acceleration response. Acceleration is a response that is related to drivers\u27 desire to attain maximum speeds which is the less urgent need than safety. Additionally, drivers\u27 response to negative stimulus is sometimes further aided by activation of brake lights for a leading vehicle that is braking. For similar safety reasons, the results show that average stimulus threshold is significantly lower for deceleration response than acceleration response and with higher magnitudes of parameters for deceleration response than acceleration response. The results also indicate that drivers\u27 behavior is significantly different for different vehicle being driven and/or followed. The results show that automobiles traveling behind large trucks have both lower magnitudes of acceleration and deceleration responses than when traveling behind other automobiles. These are unexpected results and could be due to inability of automobile drivers to see beyond large trucks in front of them. Overall, the results confirm the need for separating models for acceleration and deceleration responses and for different pairs of following vehicles because they impact drivers\u27 behavior differently. However, both the driver response time lags and stimulus thresholds are likely to depend on speed and vehicle separation. This research simplified the models and determined the driver response time lags and stimulus thresholds independent of these factors

Monitoring of Illegal Removal of Road Barricades Using Intelligent Transportation Systems in Connected and Non-Connected Environments

69A3551747117Illegal removal of road barricades without notice of road emergency officials and road users has resulted in fatalities, injuries, and property damages. It is only after an incident has occurred or someone noticed the removal and alerted the authorities for the barricade to be placed back at its intended location. Due to this event, traditional barricades must be equipped with mechanisms to alert emergency officials and warn road users of impending danger. This research utilized the Global Positioning System (GPS) module, and Radio Frequency (RF) modules to detect barricade movements, and alert emergency officials and road users. The barricade movements were estimated from the haversine distance formula, corrected for errors, and then compared with the distance threshold value for the road users within a geofenced area to be alerted. The geofenced area radius was estimated to be 1.04 miles from the barricade location using the American Association of State Highway and Transportation Officials (AASHTO), National Safety Council (NSC), and TransGuide ITS manuals. The non-parametric bootstrapping method was used to estimate the GPS position error to 10.5 feet and corrected the measured distances. Experimental data of the system from a clear sunny day shows that low-cost GPS modules have the best response to barricade movements compared to a cloudy day where movements can\u2019t be explained easily. This system can communicate with Road-Side Units (RSUs) and On-Board Units (OBUs) and is expected to warn road users and alert emergency officials

Opportunities and challenges of smart mobile applications in transportation

Smart mobile applications are software applications that are designed to run on smart phones, tablets, and other mobile electronic devices. In this era of rapid technological advances, these applications have become one of the primary tools we use daily both in our personal and professional lives. The applications play key roles in facilitating many applications that are pivotal in our today's society including communication, education, business, entertainment, medical, finance, travel, utilities, social, and transportation. This paper reviewed the opportunities and challenges of the applications related to transportation. The opportunities revealed include route planning, ridesharing/carpooling, traffic safety, parking information, transportation data collection, fuel emissions and consumption, and travel information. The potential users of these applications in the field of transportation include (1) transportation agencies for travel data collection, travel information, ridesharing/carpooling, and traffic safety, (2) engineering students for field data collection such as travel speed, travel time, and vehicle count, and (3) general traveling public for route planning, ridesharing/carpooling, parking, traffic safety, and travel information. Significant usage of smart mobile applications can be potentially very beneficial, particularly in automobile travel mode to reduce travel time, cost, and vehicle emissions. In the end this would make travel safer and living environments greener and healthier. However, road users' interactions with these applications could manually, visually, and cognitively divert their attention from the primary task of driving or walking. Distracted road users expose themselves and others to unsafe behavior than undistracted. Road safety education and awareness programs are vital to discourage the use of applications that stimulate unsafe driving/walking behaviors. Educating the traveling public about the dangers of unsafe driving/walking behavior could have significant safety benefits to all road users. Future research needs to compare accuracies of the applications and provide guidelines for selecting them for certain transportation related applications

Distracted walking: Examining the extent to pedestrian safety problems

Pedestrians, much like drivers, have always been engaged in multi-tasking like using hand-held devices, listening to music, snacking, or reading while walking. The effects are similar to those experienced by distracted drivers. However, distracted walking has not received similar policies and effective interventions as distracted driving to improve pedestrian safety. This study reviewed the state-of-practice on policies, campaigns, available data, identified research needs, and opportunities pertaining to distracted walking. A comprehensive review of literature revealed that some of the agencies/organizations disseminate useful information about certain distracting activities that pedestrians should avoid while walking to improve their safety. Various walking safety rules/tips have been given, such as not wearing headphones or talking on a cell phone while crossing a street, keeping the volume down, hanging up the phone while walking, being aware of traffic, and avoiding distractions like walking with texting. The majority of the past observational-based and experimental-based studies reviewed in this study on distracted walking is in agreement that there is a positive correlation between distraction and unsafe walking behavior. However, limitations of the existing crash data suggest that distracted walking may not be a severe threat to the public health. Current pedestrian crash data provide insufficient information for researchers to examine the extent to which distracted walking causes and/or contributes to actual pedestrian safety problems

Spatial Transferability: Analysis of the Regional Automobile-Specific Household-Level Carbon Dioxide (CO2) Emissions Models

This paper compared performance of methods for combining model information estimated in one region and applied to another region to improve estimation results. The application is for models developed to estimate household-level automobile-specific CO2 emissions. The results indicated that automobile-specific CO2 emissions models can be transferred from one geographical region to another. The estimates of CO2 emissions can assist agencies such as policy makers, businesses, and transportation planners to track trends and identify opportunities to reduce CO2 emissions and increase efficiency of transportation systems to lessen their impact on global warming, climate change, and air quality standards

Improving road safety with ensemble learning: Detecting driver anomalies using vehicle inbuilt cameras

The adoption of Advanced Driver Assistance Systems (ADAS) has expanded dramatically in recent years, with the goal of improving road safety and driving comfort. Driver monitoring is important to ADAS since it identifies abnormalities such as sleepiness, distraction, and impairment to guarantee safe vehicle operation. Traditional methods of detecting driver anomalies rely on intrusive physiological measures, while ADAS with built-in cameras offers a non-intrusive and cost-effective option. This study investigates the application of ensemble model learning for driver anomaly detection in automobiles employing ADAS and in-vehicle cameras. Deep learning models such as ResNet50, DenseNet201, and Inception V3 were deployed as learner models to classify driving behavior. The raw dataset used in this study was in the form of videos obtained from the National Tsinghua Driver Drowsiness Detection (NTHUDD) dataset. Amongst the two ensemble models used, the eXtreme Gradient Boost (XGBoost) classifier pooled predictions from the learner models. It attained a remarkable average accuracy and precision of 99% on the validation dataset. Classes such as laugh_talk and yawning were properly and separately distinguished. The ensemble technique capitalized on the strengths of various models while mitigating their weaknesses, resulting in robust and trustworthy forecasts. The findings highlight the potential of ensemble modeling to enhance driver anomaly detection systems, providing valuable insights for improving road safety. By continually monitoring driver behavior and detecting abnormalities, ADAS can provide timely warnings and interventions to prevent accidents and save human lives