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Coordinating, Developing, and Delivering Highway Transportation Innovations

 
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Publication Number:  FHWA-HRT-13-097    Date:  September 2014
Publication Number: FHWA-HRT-13-097
Date: September 2014

 

Analysis of Network and Non-Network Factors on Traveler Choice Toward Improving Modeling Accuracy for Better Transportation Decisionmaking

CHAPTER 9. CONCLUSIONS AND RECOMMENDATIONS

This chapter provides an overall summary of the activities undertaken as part of the study, highlights the principal accomplishments contributed through the work undertaken in conjunction with this effort, extracts the main lessons learned, and provides suggestions for next steps intended to advance the state of the art and practice in modeling traveler choice for the purpose or analysis and evaluation of operational and policy interventions.

SUMMARY AND ACCOMPLISHMENTS

The goal of this study was to address the important gap in modeling capability to support a variety of initiatives that seek to improve traffic conditions, system safety, and sustainability by targeting user choices before and during travel. The main emphasis of this effort was on travelers’ higher-level predictive strategic choices because these might be influenced by a range of variables including experienced system performance through the level of service, environmental factors such as weather that affect both system performance as well as activity engagement opportunities, availability and accessibility to alternative modes, quality of the walking environment, as well as measures such as pricing, information supply, dynamic traffic management, etc. A thorough understanding of the determinants of travel choices and behavior and an operational ability to model their dependence on key attributes of the transportation system, network performance, as well as non-network factors, will provide a foundation for designing effective interventions to improve system performance and for evaluating different policies and options by predicting how users will respond to these measures.

This broad goal was first supported through a literature review of network and non-network factors influencing travel behavior in the short, medium, and long terms. In a way, the scope of the present effort covers the entire realm of transportation systems analysis, planning, and operations. A comprehensive conceptual framework was articulated to highlight the principal behavior dimensions and how these interrelate with network performance to determine the impact and effectiveness of a wide range of demand-side and supply-side measures. While the framework provides the structure of a modeling capability to address this wide range of possible questions, no single modeling platform can have both the scale and the appropriate level of detail and focus to address all questions and interventions. In any modeling exercise, some aspects of the system, including traveler decisions, are considered given and fixed, while others are allowed to change and respond to the particular measures under consideration. Physicists have long differentiated between slow-changing and fast-changing dynamics, each requiring different modeling approaches and data observations. Accordingly, this study sought to demonstrate opportunities for improving modeling capabilities with respect to various policy and operational interventions by defining selected case studies. For each case study or scenario, specific modeling tools were elaborated by integrating traveler choice models in system simulation tools and demonstrated to evaluate the effectiveness of the relevant interventions.

The case studies ranged from long-term policy influences of non-network interventions (i.e., walkability and crime) on mode choice to short-term en-route behavior of speed compliance as part of INFLO speed harmonization measures. To cover these different time frames of user behavior adjustments to management and policy strategies, different models were developed, as no single modeling approach fits all purposes. Moreover, all cases model and treat individual behavior in a completely disaggregated manner. However, depending on the focus of the intervention, scale of application and resulting size of the problem, the case study models range from macroscopic to microscopic representation. Where predictive capabilities were needed, well-calibrated statistical models were used. In the case of speed harmonization, the physics of the simulation became of primary importance, and detailed microsimulation tools were used to simulate the user behavior and the associated interactions in the traffic stream. What became clear through all of the case studies is that information and how information is processed is the primary consideration for most of the management strategies. Current statistical models are limited in the way they model the diffusion and processing of information in a connected world. For that purpose, an agent-based model was developed to demonstrate how different processes could be implemented to represent information and attitude diffusion processes. Each of the case studies is summarized in the following subsections.

Urban Policy and Non-Network Interventions Case Study

Early studies of land use and travel behavior focused on hypothesis testing regarding the correlation between built environment and travel. The debate about causality of observed correlations is ongoing. Despite the large number of existing studies, the magnitude of the effects of built environment on travel behavior, specifically mode choice, is unclear. Instead of treating land use in broader categories, this case study analyzed the direct causal relationship of safety perception and walkability on mode choice for the first time. The influence of walkability and safety perception was included in an extensive mode choice model as latent variables to complement all the standard variables such as level of service and demographic variables. In addition, the mode choice model included time-varying level of service attributes.

The case study demonstrates how available data sources can be tapped, reconciled, and implemented into available model structures. It also shows the significant influence of disaggregated non-network factors on mode choice.

ATDM Case Study

This case study focused on identifying information and data that can inform understanding of the factors underlying traveler choices to use bicycling as an active transportation mode and the development of models of bicycle mode shift and usage patterns that may be incorporated in regional and operational travel demand forecasting frameworks. The examination included a review of information and data collected by local areas in regional case studies consisting of the following four urban metropolitan regions: Washington, DC, metropolitan region, Southern California metropolitan region (SCAG region), San Francisco Bay area, and the Cleveland, OH, region. Data collected from these regions confirm that bicycle travel is increasing both as an active transportation mode and as a means of travel demand management. However, bicycle travel supply and demand variables collected by local agencies vary considerably in quality and robustness. While leading edge travel demand modeling agencies are beginning to integrate bicycle use data into travel forecasting, significant data gaps limit the ability to fully incorporate bicycling choice and use in activity-based models of travel demand.

Examples from the four metropolitan study areas were presented, focusing on overall bicycle use and limited evidence for potential modal shift in connection with bike on transit service options and bike sharing plans. The importance of factors such as weather in bicycle use decisions is strongly evident through the available data. Recommended data needed to advance the state of the art and the practice were identified and presented.

AERIS Case Study I: Social Networks and Green Behaviors

Attitudes and information can influence individuals’ choices on many different levels, but little is known on how information disseminates and attitudes are formed. Management strategies aim to influence user behavior. As a result, attitudes cannot be treated as static and given. This case study developed an agent-based model of information diffusion and attitude formation. The model includes the following three main models:

Experiments conducted with the developed model demonstrated how the effectiveness of targeted information campaigns to change behavior could be assessed through their impact on opinion and attitude formation and change through agent interaction, word of mouth, and/or social media.

AERIS Case Study II: INFLO and Speed Compliance

Connected vehicle technology enables improvements in flow quality, safety, and sustainability through better driver decisions. Speed harmonization, like ramp metering or VMSs, requires drivers to comply with the advised policy in order to be effective. This case study models speed harmonization and its effect on the system performance and examines its robustness in relation to driver compliance behavior.

The individuals’ behavior was simulated with an acceleration and episode duration model. A shockwave detection algorithm was implemented to trigger speed harmonization in real time. By incorporating and calculating emissions based on the motor vehicle emission simulator, the impact of speed harmonization on emissions, in addition to travel time and flow quality, was modeled and demonstrated for a real-world scenario. The study shows that with a compliance level of around 20 percent, nearly the full benefit from speed harmonization can be achieved. The results indicate that even low levels of compliance with the suggested speed limit are sufficient for the near-success of the system. However, the minimum required compliance level varies based on the geometric characteristics of the highway segment and its flow rate.

WRTM Case Study

This case study investigated WRTM strategies in terms of their impact on flows and service levels in the network and how demand management strategies can help maintain acceptable levels of service in the transportation network during bad weather conditions. In order to do so, mode choice, departure time choice, trip cancellation, and trip shifts were studied and included in a simulation of the Chicago, IL, network. Nine combinations of these different choice levels were analyzed to recommend a mix of strategies.

Whereas during bad weather a travel time increase of 27 percent was simulated, it was demonstrated that the level of service during bad weather could be improved to the same level of service as during clear weather conditions by decreasing the demand by around 15 to 20 percent. The study showcased that such a demand decrease could be achieved by promoting alternative modes and policy interventions. A combination of information about expected bad weather travel time reaching 50 percent of travelers, and policies of delayed school openings are able to achieve about 18 percent demand reduction.

These results depict a comprehensive list of demand management and policy strategy scenarios evaluated in this study and the corresponding improvement for network-wide travelers. In general, strategies that target mode choice as well as effective policies that target peak spreading have good potential to improve the cost and reliability of travel by reducing travel time.

ICM Case Study

The last case study analyzed data from the Interstate 5 corridor in Seattle, WA, which is managed by an automated ATMS along the corridor. The case study provides a summary of how information interventions are related to travel behavior variability.

In the corridor, the following phenomena were observed, confirming the importance of understanding behavioral responses of travelers to management interventions:

LESSONS LEARNED AND NEXT STEPS

Lessons learned from these case studies include the following:

Next Steps

In terms of next steps for advancing the underlying body of knowledge and toolkit available to understand and represent traveler choice behavior in simulation and analysis tools, the priority and opportunity areas identified by the expert panels convened in the first phase of this study provide a blueprint for a research agenda for the field.

In terms of capturing and modeling behavioral phenomena, the following items are important in terms of addressing critical knowledge gaps and are particularly relevant from the standpoint of the interventions of interest while being amenable to significant practical advances:

With regard to methods for studying and modeling behavior, the following items are of importance:

 

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