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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 [...]

Demand uncertainties are inevitable in transportation networks. The transit priority network design problem over more than a decade of development has been solved under deterministic conditions. This paper proposes a model to find the optimal transit priority scheme in a multimodal transportation network under uncertain demand. This model is formulated as a risk-based bi-level optimization problem. At the upper-level, a risk measure of expected social cost is minimized subject to a chance constraint on total travel time with a user-specified confidence level and a budget constraint. At the lower-level, a mode choice, a traffic user equilibrium assignment, and a transit assignment are applied. An ant colony algorithm is utilized to solve this complex design problem. Numerical results using a real world middle-size city network empirically demonstrate that the demand uncertainty has a significant impact on the solution and the proposed model is applicable to realistic networks. [ABSTRACT FROM AUTHOR]

Urban commuters and road authorities are interested in knowing how well their city can manage traffic conditions. It is challenging to make a fair comparison among global cities due to the absence of a consistent, publicly accessible and inexpensive framework. This study proposes a methodology to compare travel time and average speed in different cities. Large cities have a central core, in which many of the major activities take place. This core is called downtown or the central business district (CBD). In this study, a comparison is made by simulating the morning commute trips from origins outside to destinations inside the CBD. The proposed framework outlines how the city and the CBD borders are defined. The data are collected by an accessible location service (Google Maps Distance Matrix Application Programming Interface). The framework is conducted on 11 major cities from the five continents, namely Cairo, London, Los Angeles, Melbourne, Moscow, New York, Paris, Singapore, Sydney, Tehran and Toronto. As such Singapore and Cairo had the shortest, while London and Paris had the longest travel times. According to the distribution graphs of average speed and travel time, Asian and African cities experienced a relatively higher average speed and a lower travel time. [ABSTRACT FROM AUTHOR]

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]

This paper proposes a method to impute the distribution of trip purposes and sociodemographic characteristics of passengers at public transit stops. Firstly, the probability of performing different types of activities around transit stops is calculated considering available land use types, distance to stops, and size of related facilities. Secondly, the temporal distribution of sociodemographic and trip purpose types of passengers in the whole network over time slots is extracted. Thirdly, the extracted temporal distribution is allocated to the location of stops based on available land use types around the stops. A household travel survey dataset from South East Queensland is chosen for the case study. The proposed method could extract valuable information for public transit authorities from spatially poor travel survey datasets and transform those datasets into a new source of information for planning and running novel applications in the public transit network. [ABSTRACT FROM AUTHOR]

How you travel, where, when, and what you do could indicate who you are. This paper discovers a possible social network between public transit passengers and develops a location–time–activity-based friend recommendation (LTAFR) model based on trips and activities of the passengers. First, trips and activities of passengers are reconstructed from the smart card data. Second, the similarity between passengers is measured in two steps for the activity similarity and trip similarity. The activity similarity is measured considering three dimensions of activity (location, time, and type). The trip similarity is measured considering both spatial and temporal dimensions. Third, a similarity score is defined as the multiplication of the activity and trip similarity values. To discover mutual relations between the passengers, the cosine similarity index is used. Finally, connected Top-k passengers are recommended as potential friends based on the highest cosine similarity values. The proposed model is implemented on a one-day smart card dataset from Brisbane, Australia. Also, the model is implemented on a household travel survey (HTS) dataset for comparing sociodemographic attributes of the recommended passengers. In the end, further investigations show that recommended potential friends have close sociodemographic attributes. [ABSTRACT FROM AUTHOR]

This paper describes a practical automated procedure to calibrate and validate a transit assignment model. An optimization method based on particle swarm algorithm is adopted to minimize a defined error term. This error term which is based on the percentage of root mean square error and the mean absolute percent error encompasses deviation of model outputs from observations considering both segment level as well as the mode level and can be applied to a large scale network. This study is based on the frequency-based assignment model using the concept of optimal strategy while any transit assignment model can be used in the proposed methodological framework. Lastly, the model is validated using another weekday data. The proposed methodology uses automatic fare collection (AFC) data to estimate the origin–destination matrix. This study combines data from three sources: the general transit feed specification, AFC, and a strategic transport model from a large-scale multimodal public transport network. The South-East Queensland (SEQ) network in Australia is used as a case study. The AFC system in SEQ has voluminous and high quality data on passenger boardings and alightings across bus, rail and ferry modes. The results indicate that the proposed procedure can successfully develop a multi-modal transit assignment model at a large scale. Higher dispersions are seen for the bus mode, in contrast to rail and ferry modes. Furthermore, a comparison is made between the strategies used by passengers and the generated strategies by the model between each origin and destination to get more insights about the detailed behaviour of the model. Overall, the analysis indicates that the AFC data is a valuable and rich source in calibrating and validating a transit assignment model. [ABSTRACT FROM AUTHOR]

While transit exclusive lanes are statically introduced for an entire day (or for the peak period), the total number and distribution of trips vary from one hour to another during a day. In this study, a Transit Priority Network Design Problem (TPNDP) under variable demand has been formulated by a bi-level robust optimization approach. At the upper level, a non-linear mathematical program is adapted to find the optimum set of exclusive bus lanes at the network level such that the total travel time as well as its hourly variation is minimized and at the lower level, a four-step demand model is applied. The results show the importance of considering the hourly travel demand variation in designing a transit priority scheme and how such results can be compared with previous TPNDP models with a fixed demand. The proposed algorithm identifies the location of full time and part-time transit lanes in the network. [ABSTRACT FROM AUTHOR]

This paper proposes a model to find the optimal location of autonomous vehicle lanes in a transportation network consisting of both Autonomous Vehicles (AVs) and Human-Driven Vehicles (HDVs) while accounting for the roadway capacity variation. The main contribution of the model is considering a generalized definition of capacity as a function of AV proportion on a link and incorporating it into the network design problem. A bilevel optimization model is proposed with total travel time as the objective function to be minimized. At the upper-level problem, the optimal locations of AV lanes are determined, and at the lower level which is a multiclass equilibrium assignment, road users including both AVs and HDVs seek to minimize their individual travel times. It is shown that if capacity variation is ignored, the effect of AV lane deployment can be misleading. Since there will be a long transition period during which both AVs and HDVs will coexist in the network, this model can help the network managers to optimally reallocate the valuable road space and better understand the effects of AV lane deployment at the planning horizon as well as during the transition period. Employing this model as a planning tool presents how the proposed AV lane deployment plan could consider the AV market penetration growth during the transition period. Numerical analysis based on the Sioux Falls network is presented in two cases with and without variable capacity to illustrate the application of this model. At the 60% penetration rate of AVs, the improvement in total travel time was 3.85% with a fix capacity while this improvement was 9.88% with a variable capacity. [ABSTRACT FROM AUTHOR]

Previous studies reveal that a loss in Modulus of Resilience (Mr) has an adverse effect on pavement performances. However, no study investigated this during extreme moisture intrusion at granular and subgrade layers, which may be compared with a flooding situation. This study has aimed to assess the impact of change in Mr due to moisture increase on pavement performance using the HDM-4 and AASHTO 2008's roughness prediction models. As a case study, the paper utilises seven selected flexible pavement road groups of Queensland, Australia that cover 50% of the major road network. Both the results match closely when pavement strength data are accurate. The deterioration predictions are verified with actual after flood data and t-tests for two major roads in Logan City, and found consistent. The study has observed some interesting findings on change in degree of saturation, pavements flood resilience and life-cycle performances with flooding scenarios. It is believed that these results would be useful in obtaining accurate ΔIRI change at varying Mr values due to a flood to select appropriate post-flood rehabilitation. A road authority can use pavements flood-resistance and life-cycle performance results for deriving a maintenance strategy. Moreover, pavement flood resilience, relationships between Mr loss and strength and Mr loss and deflection would help in pavement design, estimating existing structural strength and rehabilitation design. Finally, the paper highlights major assumptions and suggests some useful future works. [ABSTRACT FROM AUTHOR]