Delay-based Passenger Car Equivalent at Signalized Intersections in Iran

Due to their different sizes and operational characteristics, vehicles other than passenger cars have a different influence on traffic operations especially at intersections. The passenger car equivalent (PCE) is the parameter that shows how many passenger cars must be substituted for a specific heavy vehicle to represent its influence on traffic operation. PCE is commonly estimated using headway-based methods that consider the excess headway utilized by heavy vehicles. In this research, the PCE was estimated based on the delay parameter at three signalized intersections in Tehran, Iran. The data collected were traffic volume, travel time for each movement, signalization, and geometric design information. These data were analysed and three different models, one for each intersection, were constructed and calibrated using TRAF-NETSIM simulation software for unsaturated traffic conditions. PCE was estimated under different scenarios and the number of approach movements at each intersection. The results showed that for approaches with only one movement, PCE varies from 1.1 to 1.65. Similarly, for approaches with two and three movements, the PCE varies from 1.07 to 1.99 and from 0.76 to 3.6, respectively. In addition, a general model was developed for predicting PCE for intersections with all of the movements considered. The results obtained from this model showed that the average PCE of 1.5 is similar to the value recommended by the HCM (Highway Capacity Manual) 1985. However, the predicted PCE value of 1.9 for saturated threshold is closer to the PCE value of 2 which was recommended by the HCM 2000 and HCM 2010.</p

Truck weight prediction modeling

Typescript (photocopy).A reasonable forecast of anticipated traffic loading's is necessary to design or rehabilitate a highway. Underestimating the load experience for a highway could result in an under designed facility, leading to the need for major unanticipated repairs. An overestimate, however, could result in the construction of over designed facilities that ties up monies badly needed for other projects. The result of this study can be helpful in providing a better understanding of prediction of truck loads and their trends for future decision making processes. This dissertation considers the single axles, tandem axles, and gross weight distributions of seven truck types at three weigh-in-motion (WIM) stations in Texas between 1977 and 1985. It also provides evaluation of the trend in average weights over this nine year period. In addition, predictability of future truck weight distributions based on the past collected data are examined. The results cast doubt on the concept of statistically sampling truck weights at a site and projecting future trends in the axle load distribution at a particular site. The results suggest that predicting axle loads at another site base and sampling of data on roadways of similar classification may not be possible with any degree of statistical reliability

Truck weight prediction modeling

Typescript (photocopy).A reasonable forecast of anticipated traffic loading's is necessary to design or rehabilitate a highway. Underestimating the load experience for a highway could result in an under designed facility, leading to the need for major unanticipated repairs. An overestimate, however, could result in the construction of over designed facilities that ties up monies badly needed for other projects. The result of this study can be helpful in providing a better understanding of prediction of truck loads and their trends for future decision making processes. This dissertation considers the single axles, tandem axles, and gross weight distributions of seven truck types at three weigh-in-motion (WIM) stations in Texas between 1977 and 1985. It also provides evaluation of the trend in average weights over this nine year period. In addition, predictability of future truck weight distributions based on the past collected data are examined. The results cast doubt on the concept of statistically sampling truck weights at a site and projecting future trends in the axle load distribution at a particular site. The results suggest that predicting axle loads at another site base and sampling of data on roadways of similar classification may not be possible with any degree of statistical reliability

How will Iranian behave in accepting autonomous vehicles? Studying moderating effect on autonomous vehicle acceptance model (AVAM)

The primary condition for society to benefit from autonomous vehicle (AV) advantages is the acceptance of these vehicles by people. In this regard, the factors which affect the acceptance of these vehicles among different countries should be identified. In previous studies, a major focus has been on developing autonomous vehicle acceptance models, neglecting the moderating variables' effect on these models. The main aim of this research is to investigate the effect of moderating variables including demographic characteristics, psychological characteristics, traffic experience collision, and travel/driving behavior on the autonomous vehicle acceptance model (AVAM). The AVAM was developed via structural equations modeling by participating 553 Tehrani citizens by extending the unified theory of acceptance and use of technology. It was indicated that the negative relationship of the perceived risk on the intention of using AVs has been higher for individualistic people, culprit drivers with a history of more than one property damage-only collision, and those without a driving license. Also, the results showed that emphasis on the advantages and benefits of autonomous vehicles in collectivist people as compared to individualistic people would lead to a greater intention to use these vehicles

Investigating speed-safety association: Considering the unobserved heterogeneity and human factors mediation effects

The relationship between mean speed and crash likelihood is unclear in the literature. The contradictory findings can be attributed to the masking effects of the confounding variables in this association. Moreover, the unobserved heterogeneity has almost been criticized as a reason behind the current inconclusive results. This research provides an effort to develop a model that analyzes the mean speed-crash frequency relationship by crash severity and type. Also, the confounding and mediation effects of the environment, driver, and traffic-related attributes have been considered. To this end, the loop detector and crash data were aggregated daily for rural multilane highways of Tehran province, Iran, covering two years, 2020–2021. The partial least squares path modeling (PLS-PM) was employed for crash causal analysis along with the finite mixture partial least squares (FIMIX-PLS) segmentation to account for potential unobserved heterogeneity between observations. The mean speed was negatively and positively associated with the frequency of property damage-only (PDO) and severe accidents, respectively. Moreover, driver-related variables, including tailgating, distracted driving, and speeding, played key mediation roles in associating traffic and environmental factors with the crash risk. The higher the mean speed and the lower the traffic volume, the higher odds of distracted driving. Distracted driving was, in turn, associated with the higher vulnerable road users (VRU) accidents and single-vehicle accidents, triggering a higher frequency of severe accidents. Moreover, lower mean speed and higher traffic volume were positively correlated with the percentage of tailgating violations, which, in turn, predicted multi-vehicle accidents as the main predictor of PDO crash frequency. In conclusion, the mean speed effects on the crash risk are entirely different for each crash type through distinct crash mechanisms. Hence, the distinct distribution of crash types in different datasets might have led to current inconsistent results in the literature

Investigating speed-safety association: Considering the unobserved heterogeneity and human factors mediation effects.

The relationship between mean speed and crash likelihood is unclear in the literature. The contradictory findings can be attributed to the masking effects of the confounding variables in this association. Moreover, the unobserved heterogeneity has almost been criticized as a reason behind the current inconclusive results. This research provides an effort to develop a model that analyzes the mean speed-crash frequency relationship by crash severity and type. Also, the confounding and mediation effects of the environment, driver, and traffic-related attributes have been considered. To this end, the loop detector and crash data were aggregated daily for rural multilane highways of Tehran province, Iran, covering two years, 2020-2021. The partial least squares path modeling (PLS-PM) was employed for crash causal analysis along with the finite mixture partial least squares (FIMIX-PLS) segmentation to account for potential unobserved heterogeneity between observations. The mean speed was negatively and positively associated with the frequency of property damage-only (PDO) and severe accidents, respectively. Moreover, driver-related variables, including tailgating, distracted driving, and speeding, played key mediation roles in associating traffic and environmental factors with the crash risk. The higher the mean speed and the lower the traffic volume, the higher odds of distracted driving. Distracted driving was, in turn, associated with the higher vulnerable road users (VRU) accidents and single-vehicle accidents, triggering a higher frequency of severe accidents. Moreover, lower mean speed and higher traffic volume were positively correlated with the percentage of tailgating violations, which, in turn, predicted multi-vehicle accidents as the main predictor of PDO crash frequency. In conclusion, the mean speed effects on the crash risk are entirely different for each crash type through distinct crash mechanisms. Hence, the distinct distribution of crash types in different datasets might have led to current inconsistent results in the literature

Estimation of the logit model for the online contraflow problem

Contraflow or lane reversal is an efficient way for increasing the outbound capacity of a network by reversing the direction of in‐bound roads during evacuations. Hence, it can be considered as a potential remedy for solving congestion problems during evacuation in the context of homeland security, natural disasters and urban evacuations, especially in response to an expected disaster. Most of the contraflow studies are performed offline, thus strategies are generated beforehand for future implementation. Online contraflow models, however, would be often computationally demanding and time‐consuming. This study contributes to the state of the art of contraflow modelling in two regards. First, it focuses on the calibration of a Logit choice model which predicts the online contraflow directions of strategic lanes based on the set of directions obtained from offline scenarios. This is the first effort to adjust offline results to be applied for an online case. The second contribution of this paper is the generation of calibration data set from a novel approach through simulation. The calibrated Logit model is then tested for the network of the City of Fort Worth, Texas. The results show a high performance of this approach to generating beneficial strategies, including an increase in up to 16% in throughput compared to no contraflow case. First published online: 10 Feb 201

Incorporating car owner preferences for the introduction of economic incentives for speed limit enforcement

Human error including driving misbehavior contributes to over 90 percent of road vehicle accidents, and speeding is considered to be risky. Smart technologies, such as Connected Vehicle System (CVS) are among the interesting technical options to improve driving behavior, and Pay-As-You-Speed (PAYS) is an effective economic incentive to reduce speed violations. We investigated the acceptability of CVS with and without the presence of economic incentives, such as PAYS, in the context of a middle-income country: Iran. We used a Zero-Inflated Ordered Probit model (ZIOP) to estimate drivers’ willingness to pay for a CVS, and a hazard-based model for predicting the incentive level needed for accepting CVS via a PAYS scheme. ZIOP model indicated that drivers with the following characteristics were more likely to pay more for CVS: having a comprehensive insurance coverage, being younger than 60 years, owning more than one car, and having older vehicles. The hazard-based model also confirmed that drivers that speed relatively often have a lower tendency to adopt CVS, and drivers who experienced an accident in the past were more inclined to adopt CVS via PAYS. Also, drivers' opinion about CVS, vehicle characteristics, demographics, and driving experience influenced the effect of PAYS characteristics on acceptability of CVS. Finally, we offer recommendations for how to effectively implement CVS, in order to significantly reduce the high fatality and accident rates in middle-income countries such as Iran.</p

Correlates of self-reported driving aberrations in Tehran: A study at the level of drivers and districts

There are relatively few comprehensive studies on driving errors and violations in Iran, a non-Western country with a high traffic fatality rate. In this study, 712 drivers completed a questionnaire at technical inspection centres and carwashes in Tehran, Iran. Respondents were asked about their demographic characteristics, accident involvement, traffic fines, and driving aberrations in the form of the Driver Behaviour Questionnaire (DBQ). The results of a principal component analysis of the DBQ showed a distinction between errors and two types of violations: speeding and non-speeding violations. Correlation analyses showed that DBQ violations were associated with a higher driving mileage, a higher education level (for DBQ speeding violations in particular), and younger age. DBQ errors were associated with risk perception, that is, the belief that one has a high probability of becoming involved in a car accident. Regression analyses showed that the DBQ speeding violations score was predictive of the number of speeding tickets and that the DBQ non-speeding violations score was predictive of involvement in minor accidents in the past three years. A correlation analysis at the level of municipal districts showed that drivers from districts with lower education and literacy levels and lower car ownership were more likely to report driving a low-cost car and had lower DBQ violations scores. These results can be interpreted as indicating that affluence enables deviant driving. We conclude that the error-violation distinction is of relevance to road safety in Tehran, both at the level of individual drivers and at the level of districts.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Human-Robot Interactio

Incorporating car owner preferences for the introduction of economic incentives for speed limit enforcement

Human error including driving misbehavior contributes to over 90 percent of road vehicle accidents, and speeding is considered to be risky. Smart technologies, such as Connected Vehicle System (CVS) are among the interesting technical options to improve driving behavior, and Pay-As-You-Speed (PAYS) is an effective economic incentive to reduce speed violations. We investigated the acceptability of CVS with and without the presence of economic incentives, such as PAYS, in the context of a middle-income country: Iran. We used a Zero-Inflated Ordered Probit model (ZIOP) to estimate drivers’ willingness to pay for a CVS, and a hazard-based model for predicting the incentive level needed for accepting CVS via a PAYS scheme. ZIOP model indicated that drivers with the following characteristics were more likely to pay more for CVS: having a comprehensive insurance coverage, being younger than 60 years, owning more than one car, and having older vehicles. The hazard-based model also confirmed that drivers that speed relatively often have a lower tendency to adopt CVS, and drivers who experienced an accident in the past were more inclined to adopt CVS via PAYS. Also, drivers' opinion about CVS, vehicle characteristics, demographics, and driving experience influenced the effect of PAYS characteristics on acceptability of CVS. Finally, we offer recommendations for how to effectively implement CVS, in order to significantly reduce the high fatality and accident rates in middle-income countries such as Iran.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and Logistic