Search

In this study, the effect of age, gender, and desired speed (DS) factors on General Motors car-following (CF) behavior was investigated. DS was defined as the speed selected by the driver in free driving situation. A low-level driving simulator was used to collect data. The CF model for each driver was calibrated by genetic algorithm. Gender and DS were effective in CF behavior, while the age factor was not. The drivers' sensitivity to the variables of speed and distance in the CF model increased with increasing the DS. The gender factor affected only the magnitude of deceleration which was higher in women. For further investigation, the effect of the desired speed on the time headway in the steady-state CF was also examined. DS factor was effective in steady-state CF behavior. As the DS increased, the time headway decreased. Examining CF threshold demonstrated that women maintained larger distance than men. Finally, it can be said that DS and gender would be more important than age to be considered in CF models., Author(s): Ehsan Ramezani-Khansari (corresponding author) [1]; Masoud Tabibi [2]; Fereidoon Moghadas Nejad [1]; Mahmoud Mesbah [1] 1. Introduction Driving behavior can be influenced by the individual characteristics of the drivers [...]

The increasing availability of public transit smart card data has enabled several studies to focus on identifying passengers with similar spatial and/or temporal trip characteristics. However, this paper goes one step further by investigating the relationship between passengers' spatial and temporal characteristics. For the first time, this paper investigates the correlation of the spatial similarity with the temporal similarity between public transit passengers by developing spatial similarity and temporal similarity measures for the public transit network with a novel passenger-based perspective. The perspective considers the passengers as agents who can make multiple trips in the network. The spatial similarity measure takes into account direction as well as the distance between the trips of the passengers. The temporal similarity measure considers both the boarding and alighting time in a continuous linear space. The spatial-temporal similarity correlation between passengers is analysed using histograms, Pearson correlation coefficients, and hexagonal binning. Also, relations between the spatial and temporal similarity values with the trip time and length are examined. The proposed methodology is implemented for four-day smart card data including 80,000 passengers in Brisbane, Australia. The results show a nonlinear spatial-temporal similarity correlation among the passengers., 1. Introduction Analysing passengers' movements in a public transit network is important in understanding passengers' travel behaviours and designing more customised public transit services. By identifying passengers with similar spatial [...]

The widespread and increased use of smartphones, equipped with the global positioning system (GPS), has facilitated the automation of travel data collection. Most studies on travel mode detection that used GPS data have employed hand‐crafted features that may not have the capabilities to detect all complex travel behaviours since their performance is highly dependent on the skills of domain experts and may limit the performance of classifiers. In this study, a genetic programming (GP) approach is proposed to select and construct features for GPS trajectories. GP increased the macro‐average of the F1‐score from 77.3 to 80.0 in feature construction when applied to the GeoLife dataset. It could transform the decision tree into a competitive classifier with support vector machines (SVMs) and neural networks that are both able to extract high‐level features. Simplicity, interpretability, and a relatively lower risk of overfitting allow the proposed model to be readily used for passive travel data collection even on smartphones with limited computational capacities. The model is validated by a second dataset from Australia and New Zealand, which indicated that a decision tree with the GP constructed features as its input has a considerably higher transferability than SVMs and neural networks. [ABSTRACT FROM AUTHOR]

Bus travel time reliability performance influences service attractiveness, operating costs, and system efficiency. Better understanding of the distribution of travel time variability is a prerequisite for reliability analysis. A wide array of empirical studies has been conducted to model distribution of travel times in transport. However, depending on the data tested and approaches applied to examine the fitting performance, different conclusions have been reported. This paper aims to specify the most appropriate distribution model for the day-to-day travel time variability by using a novel evaluation approach and set of performance measures. Two important issues are explored using automatic vehicle location data collected on two typical bus routes over 6 months in Brisbane, namely, data aggregation influences on travel time distribution and comprehensive evaluation of performance of distribution models. The decrease of temporal aggregation of travel times tends to increase the normality of distributions. The spatial aggregation of link travel times would break up the link multimodality distributions for a busway route, but unlike for a non-busway route. The Gaussian mixture models are evaluated as superior to its alternatives in terms of fitting accuracy, robustness, and explanatory power. The reported distribution model shows promise to fit travel times for other services with different operation environments considering its flexibility in fitting symmetric, asymmetric, and multimodal distributions. The improved statistic fitting can support more effective service reliability analysis. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This work examines the impact of heavy vehicle movements on measured traffic characteristics in detail. Although the number of heavy vehicles within the traffic stream is only a small percentage, their impact is prominent. Heavy vehicles impose physical and psychological effects on surrounding traffic flow because of their length and size (physical) and acceleration/deceleration (operational) characteristics. The objective of this work is to investigate the differences in traffic characteristics in the vicinity of heavy vehicles and passenger cars. The analysis focuses on heavy traffic conditions (level of service E) using a trajectory data of highway I-80 in California. The results show that larger front and rear space gaps exist for heavy vehicles compared with passenger cars. This may be because of the limitations in manoeuvrability of heavy vehicles and the safety concerns of the rear vehicle drivers, respectively. In addition, heavy vehicle drivers mainly keep a constant speed and do not change their speed frequently. This work also examines the impact of heavy vehicles on their surrounding traffic in terms of average travel time and number of lane changing manoeuvres using Advanced Interactive Microscopic Simulator for Urban and Non-Urban Networks (AIMSUN) microscopic traffic simulation package. According to the results, the average travel time increases when proportion of heavy vehicles rises in each lane. To reflect the impact of heavy vehicles on average travel time, a term related to heavy vehicle percentage is introduced into two different travel time equations, Bureau of Public Roads and Akçelik's travel time equations. The results show that using an exclusive term for heavy vehicles can better estimate the travel times for more than 10%. Finally, number of passenger car lane changing manoeuvres per lane will be more frequent when more heavy vehicles exist in that lane. The influence of heavy vehicles on the number of passenger car lane changing is intensified in higher traffic densities and higher percentage of heavy vehicles. Large numbers of lane changing manoeuvres can increase the number of traffic accidents and potentially reduce traffic safety. The results show an increase of 5% in the likelihood of accidents, when percentage of heavy vehicles increases to 30% of total traffic. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this study, the inheritance of resistance to Beet necrotic yellow vein virus (BNYVV) in accessions Holly-1-4 and WB42 was investigated. Crosses between both resistant sources and susceptible parents were carried out and F1, F2 and BC1 populations were obtained. Virus concentrations in WB42 and its F1 populations were lower than in Holly-1-4. Observed ratios of susceptible and resistant plants in segregating populations of Holly-1-4 as well as WB42 were in agreement with hypothesis of one dominant major gene. Segregation of plants in F2 populations obtained from crosses between Holly-1-4 and WB42 revealed that the resistance genes in Holly-1-4 and WB42 were nonallelic and linked loci. [ABSTRACT FROM AUTHOR]