The second deals with the design and implementation of a socio-psychometric commuting survey for MODE Shift (COSMOS). Advanced mode shift models are developed using state-of-the-art methodology to combine revealed preference (RP) and stated preference (SP) information.
Chapter Overview
Problem Statement
Classic methods of sustainable community development and planning tools for transit services are unfortunately plagued by many problems. 3 travel demand modeling provides a new dimension to improve current practice in sustainable community development and transit service planning.
Motivation
This in turn causes a poor knowledge of the demand for the new transit service and a subsequent difficulty in designing an economically viable system. This is a critical issue in transit service planning where service improvement is intended to facilitate modal shift in favor of transit.
Research Goal and Objectives
4 Therefore, conventional models of mode choice may result in misleading estimation of modal split in cases where these psychological factors are present. 5 some behavioral factors in which passengers are more (less) likely to choose (change) the modes they are already used to, which are usually overlooked in traditional choice models.
Methodology
In the SP experiment, respondents are asked to identify their propensity to perform their work trip with a non-existing transit service in the future. In an effort to use practical attribute level ranges in the SP experiment, best practices in transit service planning are used in the form of service availability standards, service frequency and progress standards, and service reliability standards (Idris et al. 2012c).
Thesis Layout
This is followed by the presentation of general information statistics on revealed preferences (RP) in section 6.3 and general information statistics on stated preferences (SP) in section 6.4. In addition, Section 7.3 provides a detailed description of the travel modes considered in the set of choices.
Chapter Overview
Transit Planning and Route Design
Current Practice in Transit Route Design
- Mathematical Approaches
- Heuristic and Evolutionary Approaches
Heuristic approaches can handle the transport route design problem and the determination of the associated service frequencies. One of the first research attempts to tackle transit route design using heuristics was (Lampkin and Saalmans 1967).
Limitations of Current Practice in Transit Route Design
- Model Practicality
- Objective Function
- Demand Treatment
- Model Realism
For simplicity, most previous approaches assumed a fixed transportation demand matrix, which is not responsive to route coordination and service quality. Furthermore, even in the studies that took demand variability into account, they failed to capture the effect of the proposed design on existing demand along the adjacent transit routes and whether the increased demand is due to a mode change or a shift in the transit route.
Current Practice in Mode Choice Modelling
Therefore, travelers always look for satisfactory solutions rather than optimal ones (Bamberg et al. 2003; Chorus and Timmermans 2009). 18 As such, conventional models of mode choice have been criticized for their poor characterization of human behavior, which reduces their ability to accurately predict passenger choices (Ben-Akiva et al. 2002).
Incorporating Behavioural Factors in Mode Choice Models
In the literature, two different perspectives were realized to include socio-psychological factors in the mode choice decision. 21 Another research has argued that not only socio-economic factors, but also socio-psychological factors influence mode choice decisions.
Current Practice in Survey Design
Furthermore, the wider the feature level ranges, the higher the efficiency of the design. Another important characteristic that significantly affects the efficiency of the design is the maintenance of feature level balance (i.e., all feature levels appear equally in the data set).
Chapter Summary
Balancing attribute levels ensures that parameters are estimated across the range of levels, rather than having data points at some of the attribute levels, and therefore provides a good basis for estimation (Caussade et al. 2005; Scarpa and Rose 2008; Bliemer and Rose 2009). In terms of the number of choice situations, research did not provide evidence of any systematic relationship between the value of the design parameter and the number of treatments (Hensher 2001b).
Chapter Overview
The Conceptual Framework
The evaluation component, on the other hand, is concerned with the assessment of the generated route design(s) considering transit demand variability between both modes and routes. The proposed learning-based mode-shifting model is built on top of the mode-shifting models developed later in Section 7.7.
Towards a Learning-based Mode Shift Model
- Modelling the Formation of Habits in terms of the Step Size Parameter (α)
- Estimating the Step Size Parameter (α)
- Modelling the Awareness Level in terms of the Temperature Parameter (τ)
- Estimating the Temperature Parameter (τ)
- Modelling the level of Information Provision in terms of the Updating Rules . 37
- Simulation Results
- PRACTICAL IMPLICATIONS
What is important is that habits only started to reform after we investigated the change in the service and were aware of the change. In other words, the agent was fully aware of the changes in the system without exploring and directly investigating the transport mode.
Chapter Summary
49 Nevertheless, conducting controlled laboratory experiments of travel behavior is suggested to specify and test the learning-based mode-shifting process and estimate its parameters under various assumptions and levels of information provision. In addition, more research is suggested to test the spatial and temporal transferability of the proposed model.
Chapter Overview
Reasons for the Investigation
It is clear that mode choice is a complex process, strongly influenced by different socio-psychological factors. Although a number of attempts have been made to incorporate psychological factors directly into the mode choice analyses, in most cases the direct effects of psychological variables are captured through the inclusion of alternative-specific constants or dummy variables without having a theoretical basis to to support the causal. relationships between latent variables (Johansson et al.
Structural Equation Models (SEMs)
It is also established that incorporating psychological factors into the utility functions of the model selection model improves its goodness of fit. 53 In view of the aforementioned, the purpose of this chapter is to identify the causal effects of several psychological aspects on behavioral choice behavior using the SEM approach.
Understanding Mode Choice Behaviour
Such intention interacts with habit (past frequency of a certain attitude) and contextual aspects that create the final behavior (eg, mode choice). Each of the three factors is indirectly measured by its own indicators, as suggested by the Theory of Interpersonal Behavior.
Data Description
Figure 4-3 Interpersonal Behavior Theory-Inspired Path Diagram As shown in Figure 4-3, intention is represented by a latent variable, which is influenced by a set of three constructs, namely attitudinal, social, and emotional factors. In addition, habit is represented by a latent variable that is indirectly measured through the frequency of past behavior as its effect.
Structural Equation Modelling
SEM Measurement Models
59 In addition, the results show that car users give more importance to the value (important) than the expected (good) components of the attitude towards the car; while they give more importance to the expectation (good) than the value (important) component of the attitude towards the transit. This could be interpreted as transit passengers being forced to use public transport for business travel; however, they may switch to the car option if available.
SEM with Latent Variables
Interestingly, Edmonton drivers can be shown to assign a strong positive weight to attitudes toward transportation, while a negative sign is associated with attitudes toward cars. Intention seems to be driven more by attitudes toward transit than attitudes toward cars.
Investigation Outcomes
As an indication of the superiority of the car as a means of transport, the final choice of transport mode is associated with negative habitual behavior towards public transport and positive habitual behavior towards car use. 63 habit factors provided evidence for the superiority of the car as a travel alternative, such that car users would use the car for almost every single trip.
Chapter Overview
Study and Survey Objectives
In the direction of mode-shift modeling, the developed research aims to collect such information among the population of interest.
Study Area
The Census Metropolitan Area (CMA) of Toronto
This increase represents the highest percentage change of 9.2% compared to a national growth of 5.9% and an average CMA growth of 7.4%. Considering its land area and population, the Toronto CMA has a relatively high population density of 865.8 persons per square kilometer, compared to a national population density of 3.7 persons per square kilometer and an average CMA population density of 249.6 persons per square kilometer.
The City of Toronto
Overall, TTC ridership accounts for more than 80% of all transit ridership in the Toronto CMA, carrying approximately 460 million customers per year, or about 1.5 million passengers on a typical weekday. TTC service coverage is largely unchanged for 18 hours of operation per day, providing transit service within a 5 to 7 minute walk of most areas in the City of Toronto.
Survey Sample Design
- Target and Survey Populations
- Sampling Method
- Sample Size Determination
- Sample Allocation Method
This number constitutes 49.98% of the total population in the city of Toronto and 53.83% of the study population. However, due attention is paid to the city of Toronto as the majority of the study population (53.83%) lies within it, as shown in Table 5-9.
Survey Instrument Design
For example, collecting non-transit users' perceptions of transit service helped generate reasonable attribute levels for each respondent in the SP scenarios presented in Section B of the questionnaire. Further, different travel time components of the transit trip (approach, wait, transfer, in-vehicle, and exit time) were included, as well as the transit fare for the public transit alternative.
Chapter Summary
Chapter Overview
General Sample Descriptive Statistics
111 Accordingly, the collected sample size n= 1,211 is allocated to each of the six strata using the N-proportional allocation for a fixed sample, as summarized in Table 6-1. However, due attention is paid to the city of Toronto as the majority of the study population (53.83%) lies within it, as shown in Table 6-2.
General Revealed Preference (RP) Information Statistics
119 The preliminary analysis of the collected data also shows that the majority (36.09%) of the households have two people (including the respondent) living in the same household, while 12.88% have only the respondent living alone, as shown in Figure 6- 6. Such income distribution may account for the high levels of vehicle ownership and the tendency to live at home in the sample.
General Stated Preference (SP) Information Statistics
123 Furthermore, research into the extent to which respondents adhere to the choices they made in the SP experiment shows that they have confidence in their decisions. The foregoing finding increases confidence in respondents' reported SP choices and subsequently confidence in the mode-shift models developed.
General Psychological Information Statistics
Overall, the charts show a common trend where travelers give higher values to the activation and potential dimensions of the affective factor compared to the evaluation and control dimensions. Importantly, transit participants give more weight to the value (important) component of the transit attitude rather than the expected (good) component.
Chapter Summary
Chapter Overview
Fundamental Definitions and Assumptions
- Unit of Travel Demand
- Trip Purpose
- Trip Time
- Study Area
The base year for the models developed is 2006, which represents the most recent year for which extensive travel behavior data for the Toronto CMA is available from both the Workplace and Travel to Work data released by Statistics of Canada in 2006, and from the Tomorrow's Transportation Survey 2006. (TTS). The 2012 multi-instrument survey consists of an observational sample) of all individuals in the employed labor force, aged 15 and over, who have a regular place of work in the Toronto CMA and excludes those who work at home.
Modes of Travel
Auto Option
134 Car passenger all the way: this means that the cause of the trip is a passenger in the car for the entire length of the journey from home to work.
Public Transit Option
Transit with NMT access: This means that the trip maker accesses the transit system by walking or cycling to a bus stop or station. Car Access Transit: This means that a passenger accesses the transit system by car as a driver or passenger to a bus stop or station.
Non-Motorized Transport (NMT) Option
Meanwhile, 10.1% of transit users (excluding GO transit) use the car, as either a driver or a passenger, to reach the transit service. The public transport option is therefore divided into transport with car access and transport with non-motorised transport (NGV) access, within the model system.
Generating Level of Service Attributes
Modelling Commuting Work Trip Mode Choice
General Model Specification
In order to formulate the probabilistic choice model, it is assumed that the distribution of the random component of utility εm is Independent and Identically Distributed (IID) Extreme Value Type I.
Empirical Analysis
The critical value (1.96) of the t-statistic with a 95% confidence limit is considered the threshold value of considering variables in the model. In order to improve the capabilities of the developed model, behavioral factors are introduced to the model structure as latent variables in the next section.
Modelling Commuting Work Trip Mode Choice with Latent Variables
General Model Specification
Psychological factors are adjusted in the model with the help of latent variables, which are expressed as a function of socio-economic and personal characteristics. Mode choice probabilities are then modeled by treating the latent variables as explanatory variables in the models.
Empirical Analysis
An RP mode choice model with latent variables was estimated using the GAUSS code for the simulated likelihood function using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) gradient search algorithm. In addition, a similar trend was found for the different travel time components (e.g., time in the vehicle, waiting time, and walking time) as that observed in the RP mode choice model.
Modelling Commuting Work Trip Mode Shift
Modelling Mode Shift for Car Users
Nevertheless, residents of the city of Toronto, where a multi-modal transportation system and supportive land use make transportation more competitive with car traffic, are more likely to break through mode barriers and switch to public transit. In addition, residents of the city of Toronto are switching to public transportation more often.
Modelling Mode Shift for Transit Riders
160 The policy implications of the previous findings call into question the loyalty of transit passengers. Mode Shift model for Transit riders (all access modes) Model 1 (SP model). Joint SP model with latent habit).
Modelling Mode Shift for Non-Motorized Transport (NMT) Users
163 It is also important to note that the availability of park-and-ride facilities as well as real-time and schedule information were not found to be relevant for mode shift to local transport. It is clear that the modeling results provided in this section allow for a better understanding of the relative importance of different transport design factors and technologies, as well as the way they influence mode shift decisions.
Models Validation and Policy Analysis
Examination of the FPM of the developed models showed that the latent habit models do not perform as well as the SP and the joint RP/SP mode shift models. Overall, the models with latent habit showed better performance than the traditional mode choice model, while they outperformed the mode shift models without latent habit.
Chapter Summary
Furthermore, the developed models provide useful information to transit planners on the relative propensity of users of different modes to switch to public transit in response to changes in the LOS attributes of public transit. On the other hand, both the SP mode shift model and the joint RP/SP mode shift model showed the lowest overestimation of the number of transit passengers.
Chapter Overview
Research Summary
In contrast to traditional RP-based mode choice models, mode shift models are developed using a state-of-the-art methodology of combining revealed preference (RP) and stated preference (SP) information. Based on the above, the developed models provide a better understanding of drivers' preferences and mode-switching behavior.
Research Contributions
The developed models showed that traditional RP mode choice models tend to overpredict transit driving at the expense of driver preference due to the lack of behavioral as well as Customer Oriented Transit Service (COTS) elements in traditional models. Further, the developed models give transit planners an idea of the power of modal attributes to attract commuters to transit.
Future Research
34;Assessing the Influence of Design Dimensions on Stated Choice Experiment Estimates." Transportation Research Part B: Methodological. 34;System Plan for California Regional Express Bus Service in the Bay Area." Transportation Research Record: Journal of the Transportation Research Board.