Quantitative assessment of effect of enforcement intensity on violation rates of vehicle weight and dimension regulations

A theory of upper bound limit describing the relationship between maximum possible violation rate as a function of the intensity of enforcement of vehicle weight and dimension regulations and indeed other transport administrative regulations is postulated. The model is an exponential function that determines the maximum violation rate for given inspection capacity. The constant of the function depends on the method of enforcement. The theory is demonstrated to be valid. It has potential for facilitating important policy issues relating to enforcement of transport administrative regulations and within the framework of infrastructure management to be addressed.

The relative effectiveness of enforcement methods is quantified by two parameters namely, the effectiveness index and the truckers’ relative perceived probability of detection. These parameters facilitate evaluation of the performance of various enforcement methods and operation strategies. It is found that patrol teams are, on average, 30 times as effective in detecting violations as continuously operated permanent weigh scales. High perceived detection probabilities are associated with high inspection capacities. To increase the perceived detection probability (reduce violation rates) therefore implies increasing the inspection capacity.     

An analysis of the effects of French vehicle inspection program and grant for scrappage on household vehicle transaction

The French government has implemented a periodical vehicle inspection program, which aims at maintaining proper functioning of the vehicle and ensuring the emissions control systems installed on the vehicle work properly. Also, an incentive program for scrapping old vehicles was introduced in 1994 through 1996 to promote the replacement of those vehicles with higher emissions by newer vehicles with lower emissions. A hazard-based duration model of household vehicle transaction behavior has been developed in this study to examine the effects of the inspection program and the grant for scrappage on vehicle transaction timing. The model is developed as a competing risks model assuming the following three types of competing risks: replacing one of the vehicles in the household fleet, disposing of one vehicle in the fleet, and acquiring one vehicle to add to the fleet. The empirical analysis is carried out using the panel data of French households' vehicle ownership from 1984 to 1998, obtained by the panel survey called Parc-Auto, which has been conducted by a French marketing firm, SOFRES, since 1976. The long panel observation period facilitates the introduction of macro-economic indicators into the model, enabling the analysis to distinguish the effects of policy measures from macro-economic factors. The empirical results indicate that the expected vehicle holding duration becomes 1.3 years longer under the inspection program than before the program commenced, given that the vehicle is replaced by another vehicle at the end of the holding duration; and that the conditional probability of replacing a vehicle aged 10 years and over becomes 1.2 times higher, and the average holding duration becomes shorter by 3.3 years, when the grant for scrappage is available.     

Fine-tuning ADAS algorithm parameters for optimizing traffic safety and mobility in connected vehicle environment

Under the Connected Vehicle environment where vehicles and road-side infrastructure can communicate wirelessly, the Advanced Driver Assistance Systems (ADAS) can be adopted as an actuator for achieving traffic safety and mobility optimization at highway facilities. In this regard, the traffic management centers need to identify the optimal ADAS algorithm parameter set that leads to the optimization of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. Once the ADAS-equipped drivers implement the optimal parameter set, they become active agents that work cooperatively to prevent traffic conflicts, and suppress the development of traffic oscillations into heavy traffic jams. Measuring systematic effectiveness of this traffic management requires am analytic capability to capture the quantified impact of the ADAS on individual drivers’ behaviors and the aggregated traffic safety and mobility improvement due to such an impact. To this end, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and state-of-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through a multi-objective optimization approach that uses the Genetic Algorithm. The developed methodology is tested at a freeway facility under low, medium and high ADAS market penetration rate scenarios. The case study reveals that fine-tuning the ADAS algorithm parameter can significantly improve the throughput and reduce the traffic delay and conflicts at the study site in the medium and high penetration scenarios. In these scenarios, the ADAS algorithm parameter optimization is necessary. Otherwise the ADAS will intensify the behavior heterogeneity among drivers, resulting in little traffic safety improvement and negative mobility impact. In the high penetration rate scenario, the identified optimal ADAS algorithm parameter set can be used to support different control objectives (e.g., safety improvement has priority vs. mobility improvement has priority).     

A planning tool for evaluating vehicles miles travelled and traffic safety forecasts of growth management scenarios: A case study of Baton Rouge and New Orleans

This study describes an adaptable planning tool that examines potential change in vehicle miles travelled (VMT) growth and corresponding traffic safety outcomes in two urbanized areas, Baton Rouge and New Orleans, based on built environment, economic and demographic variables. This model is employed to demonstrate one aspect of the potential benefits of growth management policy implementation aimed at curbing VMT growth, and to establish targets with which to measure the effectiveness of those policies through a forecasting approach. The primary objective of this research is to demonstrate the need to break with current trends in order to achieve future goals, and to identify specific policy targets for fuel prices, population density, and transit service within the two study regions. Models indicate based on medium growth scenarios, Baton Rouge will experience a 9 percent increase in VMTs and New Orleans will experience 10 percent growth. This translates to corresponding increases in crashes, injuries and fatalities. The paper provides forecasts for planners and engineers to consider an alternative future, based on desired goals to reduce VMTs and therefore improve safety outcomes. A constrained-forecast model shows a cap on VMTs and crash rates is achievable through policy that increases fuel prices, population density and annual transit passenger miles per capita at reasonable levels through a growth management approach.     

Evaluation of eco-driving training for vehicle fuel use and emission reduction: A case study in Australia

This study evaluates effectiveness of driver education teaching greater fuel efficiency (Eco-Driving) in a real world setting in Australia. The driving behaviour, measured in fuel use (litres per 100 km of travel) of a sample of 1056 private drivers was monitored over seven months. 853 drivers received education in eco-driving techniques and 203 were monitored as a control group. A simple experimental design was applied comparing the pre and post training fuel use of the treated sample compared to a control sample. This study found the driver education led to a statistically significant reduction in fuel use of 4.6% or 0.51 litres per 100 km compared to the control group.     

An eigenmodel for iterative line planning,timetabling and vehicle scheduling in public transportation

Planning a public transportation system is a multi-objective problem which includes among others line planning, timetabling, and vehicle scheduling. For each of these planning stages, models are known and advanced solution techniques exist. Some of the models focus on costs, others on passengers’ convenience. Setting up a transportation system is usually done by optimizing each of these stages sequentially.In this paper we argue that instead of optimizing each single step further and further it would be more beneficial to consider the whole process in an integrated way. To this end, we develop and discuss a generic, bi-objective model for integrating line planning, timetabling, and vehicle scheduling. We furthermore propose an eigenmodel which we apply for these three planning stages and show how it can be used for the design of iterative algorithms as heuristics for the integrated problem. The convergence of the resulting iterative approaches is analyzed from a theoretical point of view. Moreover, we propose an agenda for further research in this field.     

Development of a simulation platform for safety impact analysis considering vehicle dynamics,sensor errors,and communication latencies: Assessing cooperative adaptive cruise control under cyber attack

While safety is one of the most critical contributions of Cooperative Adaptive Cruise Control (CACC), it is impractical to assess such impacts in a real world. Even with simulation, many factors including vehicle dynamics, sensor errors, automated vehicle control algorithms and crash severity need to be properly modeled. In this paper, a simulation platform is proposed which explicitly features: (i) vehicle dynamics; (ii) sensor errors and communication delays; (iii) compatibility with CACC controllers; (iv) state-of-the-art predecessor leader following (PLF) based cooperative adaptive cruise control (CACC) controller; and (v) ability to quantify crash severity and CACC stability. The proposed simulation platform evaluated the CACC performance under normal and cybersecurity attack scenarios using speed variation, headway ratio, and injury probability. The first two measures of effectiveness (MOEs) represent the stability of CACC platoon while the injury probability quantifies the severity of a crash. The proposed platform can evaluate the safety performance of CACC controllers of interest under various paroxysmal or extreme events. It is particularly useful when traditional empirical driver models are not applicable. Such situations include, but are not limited to, cyber-attacks, sensor failures, and heterogeneous traffic conditions. The proposed platform is validated against data collected from real field tests and tested under various cyber-attack scenarios.     

Theory of safety surrogates using vehicle trajectories in macroscopic and microscopic settings: Application to dynamic message signs controlled traffic at work zones

This paper presents a new mathematical framework for obtaining quantitative safety measure using macroscopic as well as microscopic traffic data. The safety surrogate obtained from the macroscopic data is in terms of analysis performed on vehicle trajectories obtained from the macroscopic data. This method of obtaining safety measure can be used for many different types of applications. The safety surrogate for the traffic dynamics are developed in terms of a new concept of Negative Speed Differentials (NSD) that involve a convolution of vehicle speed function obtained from vehicle trajectories and then performing the integration of the square of the output for its negative values. The framework is applicable to microscopic traffic dynamics as well where we can use car following models for microscopic dynamics or the LWR model for macroscopic dynamics. This paper then presents the use of this new safety surrogate on the development of a feedback control law for controlling traffic in work zones using Dynamic Message Signs. A hybrid dynamics model is used to represent the switching dynamics due to changing DMS messages. A feedback control design for choosing those messages is presented as well as a simple simulation example to show its application.