This thesis presents an overall picture of demographic and socio-economic characteristics as well as journey to work travel behavior characteristics in Florida. In addition, detailed comparisons of journey to work private vehicle occupancy distribution, travel time distribution, mode choice and departure time distribution by household and individual characteristics are also provided based on three data files: American Community Survey Public Use Microdata Samples data file, Summary File 3 Public Use Microdata Samples 1% and Public Use Microdata Samples 5% data files. Utilizing the three data files, this thesis not only investigates current commuting patterns but also provides more reliable information on current journey to work characteristics and helps to gain knowledge that is useful to identify problems and provide creative solutions on related transportation issues in the state of Florida. In analyzing the data, several socio-economic and journey to work travel behavior characteristics were identified. Interesting findings include the lower utilization of transit, the lower private vehicle occupancies for individuals in Florida, and different journey to work departure time distribution by gender, by young people and by senior citizens from other adult categories. The data analysis shows that the three data files reflect acknowledged demographic trends and capture known changes such as aging of population, smaller household size, and increasing car ownership. The comparison analysis shows that in most cases, ACS PUMS data files approximate SF3 PUMS 1% and 5% data files very well. The detailed comparison of the three data files regarding journey to work travel behavior characteristics in Florida is important to decision makers who will make informed choices when evaluating alternative transportation programs and related policy issues. The knowledge of reliability of data regarding journey to work travel behavior will also help transportation professionals for travel demand modeling, transportation and land use planning and related studies.

The role of public transportation increases as travel demand increases due to the growth in population and economics. The importance of providing a balanced public transportation has increased. In Ontario, Canada, the provincial government investing more than $17B in transit projects by the year of 2020 [28]. Consequently, planners and engineers motivated to pay more attention to mode split (mode choice) models used to estimate transit ridership. In most existing mode choice models, the likelihood of a trip maker using a transit mode (e.g. transit) is based on the generalized cost (GC) of using transit mode relative to the generalized cost of all other available modes. In conventional generalized cost formulations, transit costs are considered deterministic. It is quite evident, however, that great variability exists in the reliability of transit service and, as a result, the actual costs experienced by users. Efforts are ongoing to incorporate the costs of reliability in mode choice models by extending formulations to include penalties for arriving prior to or later than a desired arrival time. Transit operators strive to provide reliable service to retain and attract more users. Unreliable service can adversely affect the user by arriving late or early at their destination, waiting longer at their boarding station, and spending more time than expected in the transit vehicle. Unreliable service will also increase the user's anxiety associated with the uncertainty and discomfort. All these factors should be considered explicitly within the generalized cost (GC) function in order to accurately capture the GC of transit service relative to other modes and to ensure that these factors are not incorporated within the mode specific constant. In this study, a GC model is developed that explicitly represents service reliability. Service reliability is represented in the model as penalties associated with passengers' late arrival, early arrival, departure time shifting, waiting time, and anxiety. Furthermore, a methodology of utilizing field data to capture service reliability is defined. A Monte-Carlo simulation framework has been developed using the proposed GC function to quantify the impact of transit reliability on transit user cost. The proposed framework was applied on the iXpress service in the Regional of Waterloo in Ontario, Canada, utilizing Automated Vehicle Location (AVL) system data from the Regional Municipality of Waterloo to estimate service reliability. All the coefficients included in the proposed GC are assumed based on the relative importance of each penalty to scheduled in vehicle time by considering different passenger classes. In this research, the transit passengers are assumed to belong to one of three passenger classes based on their risk tolerance. From the results, it was found that increasing reliability of arrivals at a station can decrease transit users generalized costs significantly. We further posit that including uncertainty in the calculation of generalized costs may provide better estimates for mode split in travel forecasting models.

No description available.

The role of public transportation increases as travel demand increases due to the growth in population and economics. The importance of providing a balanced public transportation has increased. In Ontario, Canada, the provincial government investing more than $17B in transit projects by the year of 2020 [28]. Consequently, planners and engineers motivated to pay more attention to mode split (mode choice) models used to estimate transit ridership. In most existing mode choice models, the likelihood of a trip maker using a transit mode (e.g. transit) is based on the generalized cost (GC) of using transit mode relative to the generalized cost of all other available modes. In conventional generalized cost formulations, transit costs are considered deterministic. It is quite evident, however, that great variability exists in the reliability of transit service and, as a result, the actual costs experienced by users. Efforts are ongoing to incorporate the costs of reliability in mode choice models by extending formulations to include penalties for arriving prior to or later than a desired arrival time. Transit operators strive to provide reliable service to retain and attract more users. Unreliable service can adversely affect the user by arriving late or early at their destination, waiting longer at their boarding station, and spending more time than expected in the transit vehicle. Unreliable service will also increase the user's anxiety associated with the uncertainty and discomfort. All these factors should be considered explicitly within the generalized cost (GC) function in order to accurately capture the GC of transit service relative to other modes and to ensure that these factors are not incorporated within the mode specific constant. In this study, a GC model is developed that explicitly represents service reliability. Service reliability is represented in the model as penalties associated with passengers' late arrival, early arrival, departure time shifting, waiting time, and anxiety. Furthermore, a methodology of utilizing field data to capture service reliability is defined. A Monte-Carlo simulation framework has been developed using the proposed GC function to quantify the impact of transit reliability on transit user cost. The proposed framework was applied on the iXpress service in the Regional of Waterloo in Ontario, Canada, utilizing Automated Vehicle Location (AVL) system data from the Regional Municipality of Waterloo to estimate service reliability. All the coefficients included in the proposed GC are assumed based on the relative importance of each penalty to scheduled in vehicle time by considering different passenger classes. In this research, the transit passengers are assumed to belong to one of three passenger classes based on their risk tolerance. From the results, it was found that increasing reliability of arrivals at a station can decrease transit users generalized costs significantly. We further posit that including uncertainty in the calculation of generalized costs may provide better estimates for mode split in travel forecasting models.

This thesis compared revealed-preference automobile morning work commute trip data from GPS-equipped instrumented vehicles of Commute Atlanta participants with transit commute alternatives identified in the regional planning model transit network. The Transit Capacity and Quality of Service Manual (TCQSM) travel time level of service (LOS) measure for transit was applied to these GPS automobile and modeled transit data. To quantify system-level transit availability, the TCQSM service coverage LOS was applied to the Atlanta region and Atlanta s transit service area LOS was calculated as C. Most of the commuters in this study would experience transit-auto travel time LOS of F. The analyses revealed that revealed automobile travel times were 45% shorter than the model-reported automobile travel time skims for the same origin and destination zones. Transit traces, calculated by manually tracing the trips from origin to destination via the most preferable transit mode, were about 24% longer than the minimum travel-demand-modeled transit skims. Only about 9% of commuters drove directly to work more than 95% of the time and only 6% of commuters left home within five minutes of their median departure time more than 95% of the time, indicating that the convenience and flexibility of the automobile is likely to be a significant element in these commute mode decisions. Commuters perceive the total transit trip time as between being 1.25 and 2.5 as long as the actual (modeled) time, and only about 25% of commuters could take transit without having to transfer. The calculated total cost of driving to work exceeded the cost of transit, but automobile operating costs alone did not exceed transit costs for about half the sample.

This research aims at developing a better understanding of commuters preferences and mode switching behaviour towards local transit for work trips. The proposed methodological approach incorporates three main stages. The first introduces a conceptual framework for modal shift maximized transit route design model that extends the use of demand models beyond forecasting transit ridership to the operational extent of transit route design. The second deals with designing and implementing a socio-psychometric COmmuting Survey for MOde Shift (COSMOS). Finally, the third stage focuses on developing econometric choice models of mode switching behaviour towards public transit. Advanced mode shift models are developed using state-of-the-art methodology of combining Revealed Preference (RP) and Stated Preference (SP) information. The results enriched our understanding of mode switching behaviour and revealed some interesting findings. Some socio-psychological variables have shown to have strong influence on mode shift and improved the models in terms of fitness and statistical significance. In an indication of the superiority of the car among other travel options, strong car use habit formation was realized for car drivers, making it hard to persuade them to switch to public transit. Further, unlike conventional choice models, the developed mode shift models showed that travel cost and in-vehicle travel time are of lower importance compared to other transit Level of Service (LOS) attributes such as waiting time, service reliability, number of transfers, transit technology, and crowding level. The results also showed that passengers are more likely to shift to rail-based modes (e.g. LRT and subway) than rubber-tyred modes (e.g. BRT). On the other hand, the availability of park-and-ride facilities as well as both schedule and real-time information provision did not appear to be significant for mode switching to public transit for work trips. This research provides evidence that mode shift is a complex process which involves socio-psychological variables beside common socio-demographic and modal attributes. The developed mode switching models present a new methodologically sound tool for evaluating the impacts of alternative transit service designs on travel behaviour. Such tool is more desirable for transit service planning than the traditional ones and can aid in precisely estimating transit ridership.

This research aims at developing a better understanding of commuters preferences and mode switching behaviour towards local transit for work trips. The proposed methodological approach incorporates three main stages. The first introduces a conceptual framework for modal shift maximized transit route design model that extends the use of demand models beyond forecasting transit ridership to the operational extent of transit route design. The second deals with designing and implementing a socio-psychometric COmmuting Survey for MOde Shift (COSMOS). Finally, the third stage focuses on developing econometric choice models of mode switching behaviour towards public transit. Advanced mode shift models are developed using state-of-the-art methodology of combining Revealed Preference (RP) and Stated Preference (SP) information. The results enriched our understanding of mode switching behaviour and revealed some interesting findings. Some socio-psychological variables have shown to have strong influence on mode shift and improved the models in terms of fitness and statistical significance. In an indication of the superiority of the car among other travel options, strong car use habit formation was realized for car drivers, making it hard to persuade them to switch to public transit. Further, unlike conventional choice models, the developed mode shift models showed that travel cost and in-vehicle travel time are of lower importance compared to other transit Level of Service (LOS) attributes such as waiting time, service reliability, number of transfers, transit technology, and crowding level. The results also showed that passengers are more likely to shift to rail-based modes (e.g. LRT and subway) than rubber-tyred modes (e.g. BRT). On the other hand, the availability of park-and-ride facilities as well as both schedule and real-time information provision did not appear to be significant for mode switching to public transit for work trips. This research provides evidence that mode shift is a complex process which involves socio-psychological variables beside common socio-demographic and modal attributes. The developed mode switching models present a new methodologically sound tool for evaluating the impacts of alternative transit service designs on travel behaviour. Such tool is more desirable for transit service planning than the traditional ones and can aid in precisely estimating transit ridership.

There has been a noticeable swing towards school pupils being driven to and from school, and away from active modes like walking and cycling, in recent decades. This has had a number of side effects. Less reliance on active modes of transport has been a contributing factor in the reducing levels of physical activity for school children. Traffic volumes associated with school trips have also increased. This increased has tended to contribute to an increase in traffic congestion, adverse environmental effects and reductions in levels of sustainability. School trip traffic contributes specifically to congestion at school gates. Schools have been identified as having significant effects on the transportation system adjacent to them. Schools which seek Resource Consents for new or changed activities are often being required to take measures to mitigate their adverse effects The purpose of this study is to explore the factors contributing to primary school pupils' travel choices. This will help to identify travel choice patterns which may, in turn, be useful in developing policies and planning initiatives which contribute to achieving an efficient and sustainable transport system. A range of literature relevant to school and general commuting travel demand was reviewed. A case study involving the pupils of twenty two Christchurch primary schools was carried out. Pupils and their parents were surveyed to establish mode choices and the factors influencing those choices. The study found that between 55% and 60% of pupils surveyed travel to and from school by car. 30% to 35% walk or scooter, and 5% to 7% cycle. This compares with 34% travelling by car in the late 1980s. In addition, a greater proportion of those pupils who walk, scooter or cycle to school are accompanied by an adult than in the past. The results of the study also suggested that School Travel Plans, when combined with the energy and commitment to implement them can have a significant effect on school travel choices. As part of the case study, parents were asked to rank the importance of a number of factors which could influence choices regarding their children's school travel. The responses from parents identified safety concerns, regarding both road and personal safety, as the major factor behind decisions regarding their children's travel choices. Time constraints coupled with the complexity of travel requirements of many families were identified as significant factors. Multinomial Logit Models for both mode choice and pupils travel independence were then produced for both the journey to and from school. These models were based on the results of the case study. The models produced indicate that, at a school level, there is a correlation between increasing school roll and an increasing proportion of pupils travelling by car. A slight negative correlation between school decile and car usage was also indicated. This is contrary to the normally accepted understanding that in most transport situations there is a positive correlation between increasing affluence and car usage. Superior model results were obtained at a disaggregated individual level, using nine variables relating to the school, the neighbourhood, and the home, than the results obtained using the school based variables of. However, it is not considered that the effort required to obtain information on the additional variables is justified when estimating mode choices of pupils at an individual school. It is therefore recommended that a model using Decile, Average Age, and School Roll variables be used to estimate mode choices at an individual school. At a family level, there was a strong positive correlation between distance from school, age of the pupils, and the number of major roads between school and home, and car usage. It became apparent that the decisions made regarding children's school travel are very complex. Families juggle a number of factors, many of which are in conflict with one another. For example a desire to care for the environment may be in conflict with the demand to get the children to school, and get to work on time. This complex interrelationship between factors has resulted in some instances where normally accepted "Rules of Thumb", such as the understanding that increased car usage is generally associated with increasing wealth, do not appear to be applicable to school travel. The complexity of interrelationships has further meant that it has not been possible to quantify the impact of any one factor on its own.

No description available.

The general aim of this thesis was to investigate affective forecasting in the context of public transport. Paper I, Study 1 revealed that non-users of public transport were less satisfied with the services than users. It was hypothesised that non-users were biased in their satisfaction ratings, a claim that was subsequently investigated in Paper I, Study 2, where a field experiment revealed that car users suffer from an impact bias, due to being more satisfied with the services after a trial period than they predicted they would be. To address the question of whether a focusing illusion is the psychological mechanism responsible for this bias, two experiments containing critical incidents were conducted in Paper II. These experiments investigated whether car users exaggerate the impact that specific incidents have on their future satisfaction with public transport. A negative critical incident generated lower predicted satisfaction with public transport, both for car users with a stated intention to change their current travel mode (in Paper II, Study 1) and for car users with no stated intention to change their travel mode (in Paper II, Study 2), which support the hypothesis that the impact bias in car users’ predictions about future satisfaction with public transport is caused by a focusing illusion. Paper III showed that car users misremember their satisfaction with public transport as a result of their recollections of satisfaction with public transport being lower than their on-line experienced satisfaction. Additionally, the desire to repeat the public transport experience is explained only by remembered satisfaction, not by on-line experienced satisfaction. Paper IV investigated whether a defocusing technique would counteract the focusing illusion by introducing a broader context, thereby generating higher predicted satisfaction. A generic defocusing technique, conducted in Paper IV, Study 1, did not generate higher predicted satisfaction, whereas a self-relevant defocusing technique conducted in Paper IV, Study 2 generated higher predicted satisfaction with public transport. Additionally, it was found that car-use habit accounts for the level of predicted satisfaction regardless of defocusing; the stronger the car-use habit, the lower the predicted satisfaction. The conclusions from this thesis are that non-users of public transport rate the services lower than users do, and that car users become more satisfied when using the services than they predicted. These mispredictions are a result of over-focusing on a limited range of aspects in public transport (i.e., a focusing illusion). Car users’ desire to repeat the public transport experience is influenced by their inaccurate memories of the services and not by their actual experiences. However, defocusing techniques may help car users make more accurate predictions about future satisfaction with public transport; this could facilitate a mode switch from using the car to using public transport services more often. Switching to a more sustainable transport mode could be beneficial for the individual and for society.