A driving simulation experiment was conducted to test the effectiveness and relative efficiencies of two practice scenarios, each designed to present a series of lane changes for participants to negotiate. Scenario 1 included 50 lane changes and participants were instructed to follow the posted speed limits, which ranged from 48 km/h (30 mph) to 105 km/h (65 mph). Scenario 2 included 20 lane changes and participants began driving at 80 km/h (50 mph). They were told to increase their speed as they were comfortable to do so. In both scenarios, the lane changes were identified using sets of pylons flanking the desired travel lanes. Measures of steering and speed control were recorded. A cost function was developed as the product of the absolute value of the lane position of the simulator vehicle at the moment the vehicle reached each set of pylons and the square of the travel time between successive sets of pylons. The cost and cumulative cost per trial values were tested for whether they fit a power curve, indicating whether the participants learned, were still learning, or were not learning to interact with the simulator. Both scenarios were shown to be effective. The differences in the means and variances of the cumulative cost per trial values were tested. The results support the notion that Scenario 1 was more efficient, as indicated by lower variances after 20 lane changes. Additionally, the participants driving Scenario 2 had greater travel times between lane changes than those driving Scenario 1. The drawn conclusion is that directing participants to drive a range of speeds while making lane changes helps in their learningto interact with the driving simulator.

U.S. Environmental Protection Agency (EPA) states that transportation is the second leading source for air pollution. Therefore, any improvement in transportation technology can bring substantial benefits by reducing the vehicle exhaust emissions. Recently, connected vehicle (CV) technologies have become increasingly popular since their penetration to the market can bring crucial benefits. This makes it necessary to study their impact in a simulation environment to assess their benefits before their actual implementation. As such, objectives of this paper are as follows: (a) to provide a framework that can convert VISSIM vehicle trajectory output to an input for EPA’s Motor Vehicle Emission Simulator (MOVES), and (b) predict the impact of CV technologies on vehicle emissions developing an algorithm that makes benefit of a vehicle-to-infrastructure (V2I) communication application, namely Green Light Optimized Speed Advisory (GLOSA). For this purpose, an intersection is built in VISSIM, and GLOSA is implemented on a major leg of this intersection. The output data is also converted to a MOVES input file developing a new algorithm, named operating mode calculation algorithm (OMCA). Results of MOVES simulation for CO, NOx, PM2.5 and PM10 show that GLOSA application has a huge potential of reducing vehicle emission in the vicinity of traffic lights as it can lead to up-to 51.2% emission reduction. In addition, vehicle stop delay and number of stops were also reduced by 83.9% and 87.9%, respectively. Findings of the study can help understand the effect of stop-and-go driving operations on the exhaust emissions, and quantify the potential operational and environmental benefits of CVs in this context.

The purpose of this research was to develop a scenario for participants to practice steering in a driving simulator and verify the improvement in steering control over time by analyzing driving performance. This work is motivated by the need for a set of evaluation tools to determine when participants have received sufficient practice. Practice scenarios typically provide participants a fixed length or fixed time drive, or allow them to drive until they feel comfortable controlling the vehicle. These scenarios are not regularly accompanied by an analysis of driving performance and therefore do not offer the needed assurance that the participants are ready to proceed with subsequent experimental scenarios. The developed scenario provides participants a target acquisition task requiring repeated steering inputs, similar to a slalom course. This particular scenario was designed such that each participant's performance can be evaluated, free of any threshold, criterion, or benchmark value, making it applicable to any participant and driving simulator. The majority of participants who drove this scenario exhibited a remarkable improvement in steering performance, indicating a significant adjustment in their use of the steering wheel. Monitoring the improvement in driver performance will be useful in future studies to assess whether participants have received sufficient practice.

This paper proposes a procedure for determining cyclists’ fatigue state along a specific road, as a function of some external variables related to the environmental context. When the physical fatigue reaches extreme levels, the cyclist’s ability to deal with an unexpected event or with an emergency condition is particularly limited; further, poor road pavement conditions may increase occurrence probability of critical events. In order to identify the potentially most dangerous road paths, the authors defined a methodology to build a model for cyclist’s fatigue evaluation in terms of Heart Rate class. The proposed procedure is based on the collection of simple data processed by means of Pattern Recognition techniques. The main result is to identify road segments causing a relevant fatigue state in cyclists and, thus, more risks for their safety. In the most critical spots, the road managers might mitigate risk for cyclists by means of specific actions, such as proper pavement maintenance, side-element renovation, separation from vehicular traffic, etc. The first results are very interesting, as proved by the low errors (less than 8%) obtained from the model. These outcomes may be used by road administrators for identifying potentially hazardous road sections and increasing the attention on cyclists with appropriate maintenance interventions that can reduce their safety risks.

© 2019, Gioacchino Onorati Editore. All rights reserved.Speeding is an important risk factor of road traffic crashes, and identifying the factors associated with speeding is crucial for road safety. In the present study, face-to-face roadside interviews were conducted to investigate the reasons of speeding among Turkish drivers at six different data collection times in two different cities. A structured interview was administered to collect data from a total of 3317 and 1559 drivers in Ankara and Afyon cities, respectively. Results from the two cities consistently revealed that drivers’ self-reported speeding frequency was partially determined by gender and age. Also, perceiving speeding as a cause of crashes was associated with lower frequency of speeding. Finally, thinking that speed tickets are a means for increasing government income and that it would be safe to exceed the speed limit if driving “safely” was positively associated with frequency of speeding. These findings indicate the importance of norms and (biased) perceptions in speeding behavior.

The automotive industry is facing a major challenge to reduce environmental impacts. As a consequence, the increasing diversity of powertrain configurations put a demand on testing and evaluation p ...