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Modal Shift Forecasting Models for Transit Service Planning

Problem Definition

The growth of nations and the need to meet mobility have significantly accelerated urban motorization and increased the reliance on the private automobile as a travel alternative. In turn, such increment in auto dependency has provided unprecedented levels of mobility and liberty to motorists. However, the adverse effects associated with the extensive use of private cars in urban areas cannot be overstated. Obviously, the unlimited use of single occupancy vehicle has raised resource consumption, traffic congestion, and emissions. Further, it reduced the economic, social and environmental viabilities of urban communities.

Consequently, aiming at providing a better quality movement of people, transport planners’ focus has been directed, in the early 1970’s, towards managing the increasing travel demand rather than boosting supply, which is known as Travel Demand Management (TDM). In general, numerous TDM policies (e.g., congestion pricing, parking management, and public transit provision) have been adopted to achieve such objective by changing individuals’ travel behaviour from extensive automobile usage towards the use of more sustainable means of transport.

Of the TDM policies, increasing transit provision is an effective strategy that is capable of addressing many traffic and environmental problems in modern society. Public transit is a generic term involving a large family of conventional and innovative technologies complementing each other to provide system-wide mobility in urban and rural areas. Public transit enables high capacity, energy efficient and low emission movement of people. In addition, it provides auto owners who do not want to drive with an attractive travel alternative, and represents an essential service for those who lack access to private vehicles such as students, senior citizens and others who may be economically or physically disadvantaged. With no doubt, efficient transit systems affects everyone as they save time and money not only for transit riders but also for automobile drivers.

In light of the above, increasing modal shift from auto towards public transit is a desirable objective of modern public transit systems. Automobile users might consider shifting to transit if they have an affordable and a good quality system available. Transit providers attempt to maintain an attractive system by maximizing the quality of the service while minimizing its cost. This trade-off between quality and cost turns the transit planning process into a multi-objective problem where passengers’ and operator’s interests conflict. Hence, there is an urging need for proper analytical tools to aid the transit planning process.

 


Modal Shift Forecasting Models for Transit Service Planning

Significance of the Problem

There has been a growing interest in promoting sustainable communities that incorporate compact, mixed-use development and pedestrian-friendly street network design to support high-quality transit services. Such form of development is commonly referred to as Transit Oriented Development (TOD). While TOD is essential to support high-quality transit, it is not sufficient alone to achieve this goal, as elements of the transit service itself play a key role in defining transit quality. Recently, the concept of Customer Oriented Transit Service (COTS) has been promoted to further support high quality transit, with the ultimate goal of attracting auto users to transit and maintaining acceptable levels of transit ridership. COTS is characterized by fast and reliable service, passenger information systems, attractive vehicle design (both interior and exterior), distinctive and attractive station design, electronic fare collection, etc.

As noted above, the main objective of COTS is to attract and retain transit ridership while making transit a viable competitor to auto driving. COTS is now considered an integral part of sustainable transportation and community development programs. However, planning sustainable communities and designing COTS are not very straightforward tasks. The success of any sustainable community planning and COTS design relies on how the policies and design elements affect peoples’ travel choices and behaviour. Hence, without proper analytical tools of evaluating the impacts of alternative sustainable transportation policies (such as TDM policies, transit-oriented land use policies, etc.) and COTS elements (some of which are qualitative) on travel behaviour, it becomes prohibitive to assess and develop effectively successful TOD plans and COTS designs.

Unfortunately, classical methods of sustainable community development and transit service planning tools are plagued with many problems. They are generally aggregate, hence more appropriate for regional planning than community/neighbourhood planning. Moreover, conventional mode choice models often overestimate mode shift to transit and are not sensitive to customer-oriented service elements (e.g., passenger information provision, Intelligent Transportation Systems (ITS) technologies that improve reliability, rail vs. bus attraction, etc.). Furthermore, such models are criticized for their weak characterization of several psychological aspects, contributing in part to their misleading modal shift estimation. Nevertheless, recent research advancements in travel demand modelling provide a new dimension for improving current practice in sustainable community development and transit service planning.

Approach and Impact

In an attempt to overcome the above mentioned limitations in current practice in sustainable community development and transit service planning, this study intends to provide a better understanding of commuters’ preferences and mode switching behaviour. In particular, this research aims at developing proper analytical tools to aid the transit service planning process. Such goal is achieved through the completion of the following main objectives:

First, designing and implementing an innovative multi-instrument socio-psychometric survey to gather Revealed Preference (RP) and Stated Preference (SP) data about travellers, along with psychological information associated with different modes of travel. The developed survey is conducted online among a representative sample of Toronto commuters who are asked about their willingness to shift to different transit technologies of varying characteristics. In addition to collecting common socioeconomic, demographic and modal attributes, the survey gathered data on the revealed mode choice behaviour as well as the stated mode switching preferences to public transit considering some important preference attributes such as advance information provision, ITS technologies and rail vs. bus attraction. Moreover, different psychometric tools are used to capture psychological factors affecting mode choice. In more specific terms, the survey gathered psychological information regarding habit of auto driving, affective appraisal and personal attitudes associated with different travel options. Further, the survey set up a stated choice experiment based on efficient experimental design techniques to maximize the information gained while minimizing the number of hypothetical scenarios required. The survey respondents are asked to identify their propensity to perform their work trip by a non-existing transit service in the future. In an effort to maintain practical attribute level ranges in the stated choice experiment, best practices in transit service planning are utilized in terms of service accessibility standards, service frequency and headway standards, as well as service reliability standards.

Second, developing enhanced ridership forecasting tools for improved transit service planning. Econometric demand models of mode switching behaviour are estimated to evaluate transit investments that usually target car users. As opposed to traditional mode choice models based on RP data, adequate mode shift models are developed using state-of-the-art methodology of combining Revealed Preference (RP) and Stated Preference (SP) information to accurately forecast transit ridership. Separate mode shift models are estimated for different groups of commuters (car drivers, shared ride users, transit riders, and active mode users). The estimated models are sensitive to Level of Service (LOS) attributes of the competing options as well as socio-demographic and psychological information of the decision makers. The developed models allow us to better understand the relative importance of different transit design factors and technologies, as well as the way they influence mode shift decisions. 

Third, developing a conceptual framework for modal shift optimized transit route design model that extends the use of the developed models beyond forecasting transit ridership (demand) to the operational extent of transit route design (supply). The proposed framework is intended to generate optimal transit route designs that maximize demand attraction. The framework builds upon and extends the powerful capabilities of the existing MIcrosimulation Learning-based Approach for TRansit ASsignment (MILATRAS), to tackle the route design problem. MILATRAS currently models transit assignment given a fixed set of transit routes and transit demand. The presented framework adds a mode shift module to MILATRAS in order to find operationally implementable transit route that can provide alternative design concepts corresponding to different service requirements. Further, modal shift barriers such as attitudes and habit formation are captured in the model by specifying a threshold or inertia against shifting between modes. Transit demand variability among both modes and routes is considered at the microscopic level by running the joint mode and route choice models of MILATRAS, allowing for consistency between the supplied service level and passenger demand.

Key Findings

Econometric models of mode shift, with emphasis on capturing attitude and mode switching behaviour towards public transit, are developed. The modelling results enrich our understanding of mode switching behaviour and reveal some interesting findings.  Socio-psychological variables have shown to have strong influence on mode choice and improved the models in terms of fitness and statistical significance. In an indication for the superiority of the car among other travel options, strong car use habit formation was realized for car drivers, making it hard for them to switch to public transit. Further, unlike traditional mode choice models, the developed mode shift models show that travel cost and time are of minor importance compared to other transit Level of Service (LOS) attributes such as waiting time, service frequency, system reliability, number of transfers, transit technology, and crowding level. It was also shown that passengers are more likely to switch to rail-based modes (e.g., LRT and subway) than rubber-tired modes (e.g., BRT). On the other hand, both schedule and real-time information provision did not appear to be significant for mode shift decisions. This can lead us to argue that such factors are more important for short-term route shift rather than mode shift decisions. The previous findings unravel the reason why conventional mode choice models (based only on common socioeconomic and demographic characteristics of the decision maker and basic mode-related attributes, and lacking psychological factors) tend to overestimate mode switch to public transit.

Moreover, examining the Forecasting Performance Measure (FPM) of the developed models showed that traditional RP mode choice models have the poorest forecast ability, whereas the SP and the joint RP/SP mode shift models have the best performance. In particular, traditional RP mode choice models have shown a very high tendency to over-predict transit ridership, reaching a value of 133.89%, as shown in the figure below. Such transit ridership overestimation can be attributed to the lack of behavioural as well as Customer Oriented Transit Service (COTS) elements (e.g., passenger information provision, ITS technologies that improve reliability, and rail vs. bus attraction) in traditional models. On the contrary, the joint RP/SP mode shift models had the lowest transit ridership overestimation (5.20%), while the SP mode shift models had the highest transit ridership underestimation (-8.79%).

Interestingly, the previous observations confirm the initial hypothesis of this research that RP models tend to overestimate mode shift to transit. It should be clear that the SP data complemented the RP information and resulted in improved forecasting performance and less transit ridership overestimation. In light of the above, the developed models provide a better understanding of commuters’ preferences and mode switching behaviour.

The previous findings unravel the reason why conventional mode choice models (based only on common socioeconomic and demographic characteristics of the decision maker and basic mode-related attributes, and lacking psychological factors) tend to overestimate mode switch to public transit. The impact of these findings on policy issues is a matter that should be kept in mind, especially while modelling mode shift to transit, as it seems that car users will seldom use public transport even if a better service is provided to them given their strong car use habit formation. Apparently, demand management schema, such as promoting transit provision, might not have the expected result given the found level of attachment to the car.

 

Transit Ridership Overestimation

Potential Market

This research overcomes the previously mentioned gaps in both mode choice modelling and transit service planning using a threefold approach. First, designing and implementing an innovative multi-instrument socio-psychometric survey to collect detailed information for mode shift modelling. Second, developing enhanced ridership forecasting tools for improved transit service planning. Third, developing a conceptual modal shift optimized transit route design model that extends the powerful capabilities of the existing MIcrosimulation Learning-based Approach for TRansit ASsignment (MILATRAS) to tackle the route design problem.

By unraveling the reasons why conventional mode choice models tend to overestimate mode switch to public transit and explicitly considering the multi-objective nature of the transit route design problem, the developed approach represents a practical transit route design tool that is more desirable for transit planners. Such tool can aid the transit planning process in evaluating alternative emerging technologies that target car users, such as passenger information systems, ITS technologies and new transit infrastructure development strategies (e.g., LRT, BRT, High Speed Rail, etc.).

 


Literature And Background

Over the decades, research has continuously improved mode choice models on an analytical viewpoint in attempt to make them better explain modal split. Nevertheless, traditional mode choice models are criticized for their poor characterization of human behaviour and weakness of their assumptions. In particular, such models do not only imply rational passenger behaviour, but also complete knowledge of the transportation system and perfect information about all the available alternatives and their choice consequences. In fact, the rationality of passengers is bounded by the information they could have, the cognitive limitations of their minds and the terminable amount of time available to them to make decisions. Thus, passengers lack the ability and resources to find an optimal solution, and they instead apply their rationality only after simplifying the available travel choices. Hence, a passenger always seeks a satisfactory solution rather than the optimal one.

Numerous research efforts referred the lack of searching and processing of information to some behaviour factors of sub-optimal characteristics that could lead to the domination of a specific mode even in cases where the rational choice favours another. Further, evidence in the literature shows that traditional mode choice models fail to forecast modal shift in response to new improvements in the transit service. Such failures are generally attributed to the lack of tools that can adequately forecast the travel behaviour of potential ridership.

In more specific terms, traditional mode choice models tend to overestimate the attractiveness of public transit for choice users which leads to over predicting transit ridership. Such models are criticized for their weak characterization of several behavioural aspects, contributing in part to their misleading modal shift estimation. More recent research explicitly referred the reluctance to mode switch to some psychological aspects such as personal attitude, habit formation, and affective appraisal. Hence, conventional mode choice models may result in misleading modal split estimation in cases where those psychological factors exist. This in turn induces a poor knowledge of the demand for the new transit service and a subsequent difficulty in designing an economically sustainable system.

Moreover, it is often difficult to accommodate attributes of emerging systems and technologies such as passenger information systems, ITS technologies that improve reliability, and new transit technologies (e.g., LRT and BRT) in conventional mode choice models because detailed information of such attributes are often missing in traditional cross-sectional household based RP travel survey data. This is a critical issue in transit service planning where improving service to facilitate modal shift in favour of transit is targeted.



The Unobservable Component of Transit Utility! :D


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