Transportation Demand Management Research Articles

Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China

As the propensity to link multiple intermediate stops in a trip chain (a sequence of journeys that starts and ends at home, includes visiting one or more locations) is more prevalent, the relationship between travel mode choice and trip chain pattern aroused the attention of academics. This paper examines two distinct structures to identify the decision process of travelers between travel mode choice and trip chain pattern: one structure in which trip chain pattern organization precedes travel mode choice, another structure in which travel mode choice decision precedes the organization of trip chain pattern. To accommodate multi-day behavioral variability and unobserved heterogeneity in personal characteristics ignored by traditional travel surveys, multi-day GPS data collected in Shanghai is employed to estimate these two structures within Nested Logit (NL) model. The Monte Carlo (MC) method simulates the switch of trip-chaining and mode choice under possible Transportation Demand Management (TDM) strategies based on estimation results. The findings of this study are as follows: (1) trip chain pattern decision precedes travel mode choice, which means trip-chaining is organized first and affects travel mode choice; (2) complex trip chain is related to higher automobile dependency, and it is a barrier to the tendency to adopt public transit; (3) people who generally travel by automobiles might switch to public transit when private cars are unavailable, and an increase in household bicycle ownership enhances competition between the bicycle and public transit which leads people to turn to cycling. These findings help implement TDM strategies to develop sustainable transportation systems and optimize the urban trip structure.

Analysis of Taste Heterogeneity in Commuters’ Travel Decisions Using Joint Parking– and Mode–Choice Model: A Case From Urban India

The concept of transportation demand management (TDM) upholds the development of sustainable mobility through the triumph of optimally balanced transport modal share in cities. The modal split management directly reflects on TDM of each transport subsystem, including parking. In developing countries, the policy-makers have largely focused on supply-side measures, yet demand-side measures have remained unaddressed in policy implications. Ample literature is available presenting responses of TDM strategies, but most studies account mode choice and parking choice behaviour separately rather than considering trade-offs between them. Failing to do so may lead to biased model estimates and impropriety in policy implications. This paper seeks to fill this gap by admitting parking choice as an endogenous decision within the model of mode choice behaviour. This study integrates attitudinal factors and built-environment variables in addition to parking and travel attributes for developing comprehensive estimation results. A mixed logit model with random coefficients is estimated using hierarchical Bayes approach based on the Markov Chain Monte Carlo simulation method. The results reveal significant influence of mode/parking specific attitudes on commuters choice behaviour in addition to the built-environment factors and mode/parking related attributes. It is identified that considerable shift is occurring between parking-types in preference to switching travel mode with hypothetical changes in parking attributes. Besides, study investigates the heterogeneity in the willingness-to-pay through a follow-up regression model, which provides important insights for identifying possible sources of this heterogeneity among respondents. The study provides remarkable results which may be beneficial to planning authorities for improving TDM strategies especially in developing countries.

A Learning Framework for Personalized Random Utility Maximization (RUM) Modeling of User Behavior.

Understanding user behavior is crucial for the success of many emerging applications that aim to provide personalized services for target users, such as many patient-centered health apps and transportation apps. Models based on the random utility maximization (RUM) theory are widely used in learning and understanding behavioral preferences on the population level but find difficult to estimate individuals' preferences, particularly when individuals' data are limited and fragmented. To address this problem, our framework builds on the concepts such as canonical structure and membership vectors invented in recent works on collaborative learning and is suitable for modeling heterogeneous population with insufficient data from each individual. We further propose an extension of the collaborative learning framework using pairwise-fusion regularization as a knowledge discovery tool for real-world applications where the canonical structure is uneven, e.g., some canonical models may only represent minor subpopulations. Computationally competent algorithms are developed to solve the corresponding optimization challenges. Extensive simulation studies and a real-world application in smart transportation demand management (TDM) show the effectiveness of our proposed methods.

Transportation Demand Management through Physical Improvements: The Case of Pristina

Transportation Demand Management through Physical Improvements: The Case of Pristina

Toll roads vs. Public transportation: A study on the acceptance of congestion-calming measures in Madrid

• This article compares the acceptance of transit improvements and incentives to use an underutilized toll road. • Toll discounts are less acceptable than transit improvements, especially those related to carpooling. • Incentives to use the toll road are not enough to attract free highway drivers. • Socioeconomic factors play a smaller role compared to trip-related variables. To relieve road congestion, a variety of transport demand management measures (TDM) have been implemented all over the world. The success of these measures has been found to depend at least partially on users’ perception about them. Several articles have jointly addressed the acceptability of public transport improvement (carrot policies) and road pricing (stick policies). However, these research works have not considered discounts on existing toll roads to relieve congestion on free alternative expressways. The objective of this paper is to study the combined acceptance of different congestion-calming policies at the suburban level, including the promotion of toll roads and measures to foster the use of transit. To that end, a survey was conducted among travelers on a commuting transport corridor in the region of Madrid aimed at exploring their perceptions towards four TDM measures, and a choice modeling framework was conducted. The scenarios considered are “carrot” policies, and the results of the analysis indicate that enhancing public transportation enjoys more support among respondents than toll promotions. Motorists using the free highway are more willing to use transit improvements than to opt for a toll road as an alternative regardless of the proposed toll discounts. Among the TDM measures explored, the least supported is the promotion of toll discounts associated with non-household carpooling. Furthermore, according to the results, the adoption of these TDM measures is more influenced by trip-related factors, in particular, the trip frequency and the mode of transport, than by socio-economic characteristics of the traveler. Finally, some geodemographic attributes of the residential location are also found to be statistically significant.

Assessing the Environmental and Economic Footprint of Electronic Toll Collection Lanes: A Simulation Study

Electronic toll collection (ETC) plays, as part of transport demand management (TDM) measures, an important role in preventing traffic congestion and improving the environmental conditions in urban and rural areas. An attempt is made in the framework of this paper to evaluate the overall performance of a toll station when a lane is dedicated to ETC. The case study refers to a toll station in the Thessaloniki Metropolitan Area, Greece. Scenarios considered specific traffic characteristics, variable toll booth setups, and different penetration rates of the ETC tag users for car and heavy vehicles. The tool used in the evaluation process was the PTV Vissim traffic simulation software. The operation of the toll station during a specific peak-hour period was simulated with the aid of the specific software. In total, 39 alternative scenarios were developed and compared to determine the level of penetration rate for which the ETC lane would be effective for different toll booth setups. Results showed that when the right lane of the toll station is converted to ETC lane, the penetration rate of this lane must be greater the 15% for the private vehicles and 20% for the heavy goods vehicles (HGV) to reduce traffic congestion and to improve environmental conditions. It was also found that when an additional ETC lane was introduced to the existing toll station set up, traffic congestion and the associated environmental conditions were much improved even for low penetration rates. It must be noticed that the results from the use of discounted cash flow methods like internal rate of return (IRR), net present value (NPV) and benefit–cost ratio (BCR) showed that all economic indicators converge as penetration rate increases in all toll booth setups. Therefore, there is a specific penetration rate threshold above which the economic viability of the investment is secured. These findings can assist the design of an effective policy in terms of the optimized operation of a toll station and sustainable mobility planning.

An online updating method for time-varying preference learning

The rapid proliferation of smart, personal technologies has given birth to smart Transportation Demand Management (TDM) systems that can give personalized incentives to users. This personalization capacity builds on accurate modeling of user behaviors; however, in practice, a user’s behavior data is often limited, and his preferences in the discrete choice-making process may change or evolve. In this paper, we propose a new online-updating model that can accurately and efficiently estimate an individual’s preferences from his discrete choices. Our model is built on the concept of canonical structure, where a set of canonical models are identified as the common preference patterns shared by the whole population, and a membership vector is also identified for each individual to capture the degrees of the resemblance of his preferences to those common preference patterns. To allow preference to vary in the choice-making process, a time-varying model can be integrated with the canonical structure. In the current study, we use a simple cubic polynomial model with a single variant and show the detailed formulation of the integrated model. An online-updating strategy is also proposed, such that it is possible to update the parameters partially in practice. The proposed model is suitable for modeling a heterogeneous population with insufficient data from each individual. Both simulation studies and a real-world application are taken in the current study. The results show that comparing with other frequently used models, the model we proposed has the highest accuracy in preference learning and behavior prediction.

Behavioral response to promotion-based public transport demand management: Longitudinal analysis and implications for optimal promotion design

Abstract Increasing ridership in the urban rail systems in major cities is outpacing their designed capacity. Promotion based demand management can facilitate better utilization of the available capacity of the existing system when the investment and opportunity to expand the system are limited. While several studies address short-term behavioral responses to such promotions using before and after analysis, how behavioral changes are sustained in the long run is also very important as well as differences in response among different user groups. Using an extensive smart card dataset over two years from Hong Kong’s urban heavy railway system, this paper explores the longitudinal behavior of passengers in response to a promotion aiming at changing passengers’ travel period from peak to the pre-peak. The approach uses customer segmentation to understand the heterogeneous response of different groups. Users who have high flexibility in schedule and familiarity with the system and travel long distances tend to be more likely to change their travel periods to take advantage of the discount. The longitudinal promotion analysis reveals that 35–40% of passengers who initially adopted the promotion will eventually revert to their previous travel time periods. The results suggest that the promotion designs should be adjusted/renewed over time to sustain the initial response given the attrition of early adopters. Based on the behavioral analysis, an ‘optimal’ promotion design approach is applied to examine the effectiveness of promotion strategies given different behavioral responses over time, heterogeneous group behavior, and constraints on the investment budgets and performance requirements. The promotion design using group-specific response can better target price-sensitive users, hence improves its effectiveness over time, while the design based on the average response shows a significant performance decrease. However, the optimal design schemes using different behavioral responses are relatively consistent in terms of the selected stations for promotion, though some differences exist in the discount levels and time periods for the areas where there can be more early morning travelers. From a design perspective, there is not much difference in the promotion effectiveness regardless of the behavioral response assumed for the design.

A Review of Automatic Travel Mode Detection Methods

Household trip data is of crucial importance for managing present transportation infrastructure as well as to plan and design future facilities. It also provides basis for designing new policies, implemented under Transportation Demand Management, and assessing their effectiveness. With passage of time, methods used for household trip data collection have evolved, starting from the conventional face-to-face interviews or paperand-pencil interviews, moving on to mail-back surveys and internet-based surveys, before finally reaching to the recent approach of passive data gathering. Recording travel data automatically will require the use of modern technology present in the form of various sensors, and employing intelligent algorithms to infer the required information from these sensors’ data. These sensors can be integrated into a purpose-built device or more recently can be present in smartphones. The current study provides a comprehensive review of the research done in the field of travel mode detection from data passively collected with the help of various devices. The review starts from Global Positioning System (GPS) loggers and moves to cover purpose-built wearable devices containing additional sensors and finally ending with the most modern approach of incorporating smartphones. The summary tables presented in this study are of great value to the researchers trying to get insight of this research field.

Modelling the Modal Shift Effects of Converting a General Traffic Lane into a Dedicated Bus Lane

This paper presents an analytical framework for evaluating the performance of dedicated bus lanes. It assumes that under a designated travel demand, the traffic volume on a corridor changes with the modal shifts. The modal shift affects the operations of both bus traffic and car traffic and eventually, an equilibrium bus share ratio that maximizes the performance of the corridor will be reached. Microsimulation modelling is employed to assess the traffic operations under various demand levels and bus share ratios. The results show that converting a general lane into a bus lane significantly reduces bus delay. For car traffic, the overall trend is that delay increases after converting a general lane to a bus lane. In addition, delay decreases with the increase of bus share ratio. Nevertheless, when bus share ratio reaches 0.6 (demand less than 10,000 passengers per hour, pph; or 0.8 when demand increases up to 14,000 pph), there is no significant difference in delay between the two scenarios. The identified bus share ratios have the potential to direct the development of bus lane warrants. Finally, this research recommends that the Transportation Demand Management (TDM) strategies shall be developed to stimulate the modal shifts towards the identified optimal bus share ratio.

Freight and Service Parking Needs in Historical Centers: A Case Study in São João Del Rei, Brazil

The objective of this study is to analyze the demand for loading and unloading parking spaces in the center of São João Del Rei, a historical city in the State of Minas Gerais, Brazil, through freight trip generation models. To generate the models, the number of employees is used as an independent variable. Results show that the historical center receives an average of 710 freight trips per day, which would require at least 43 spaces for loading and unloading. As the center has only eight such spaces available, representing 18% of total demand, this study proposes new locations and suggests transportation demand management measures that could be used in conjunction with the allocation of new parking spots.

Traveler’s Willingness to Accept and Provide Carpooling Services: A Case Study in Beijing

AbstractCarpooling as a transportation demand management (TDM) tool is currently being prevalent in major Chinese cities and producing much diminishment in the frequency of solo-driving trips. Meanwhile, much disputes relating to carpooling is arising. To better understand the acceptance and the influence factors of carpooling, this paper investigates travelers’ willingness to provide and accept carpooling services. Firstly, a questionnaire survey was conducted. Secondly, we proposed a theoretical model, both car-owners and non-car-owners were sampled as respondents, and a multi-variable regression method was employed to analyze the survey data. Finally, we found that the higher acceptance, the more positive reactions to carpooling. The results indicate that it is necessary to improve the public’s acceptance of carpooling, because lower acceptance will lead to more negative reactions towards the carpooling, which may weaken its effectiveness.

Reductions in CO2 Emissions from Passenger Cars under Demography and Technology Scenarios in Japan by 2050

Climate policy requires substantial reductions in long-term greenhouse gas (GHG) emissions, including in the transportation sector. As passenger cars are one of the dominant CO2 emitters in the transport sector, governments and the automobile industry have implemented various countermeasures, including decarbonization of fuels, more energy efficient vehicles, and transport demand management. However, the total impact of these measures in the long term remains unclear. This study aims to clarify the CO2 emissions reductions from passenger cars by 2050 in 1727 municipalities in Japan under a declining population. To estimate CO2 emissions, we model travel behavior and traffic situations reflecting the regional conditions of the municipalities, including population density and accessibility to public transport for the base year 2010. Assuming plausible scenarios for future populations and automobile technologies, we estimate CO2 emissions from passenger cars. We estimate that CO2 emissions will decline by 64–70% between 2010 and 2050, with automobile technologies playing the largest role. We find that the impact of urban compaction is marginal at the national level but varies by municipality. These results imply that, given regional variations, all countermeasures, including technology and demand management, must be used to achieve the long-term target of CO2 emissions reductions.

Method development for workplaces using mobility plans to select suitable and sustainable measures

This paper aimed to provide a set of sustainable measures suitable for workplaces according to local requirements. To support this, a specific Transportation Demand Management strategy was applied, which is called Workplace Travel Planning. The result of such a Workplace Travel Plan is a package of measures implemented by an organization to encourage sustainable commuting. This research focused on two stages of the planning process: in the analysis stage, data were gathered from the stakeholders, and travel behavior was analyzed, while in the planning stage, a specific set of measures was proposed to the workplace. The selection of those measures relies on several aspects, such as existing workplace infrastructure, employer policies, employee requirements, local infrastructure, cost of implementation, level of sustainability. The mobility questionnaires are the instruments used to retrieve the input data used in the method, while the categorization of measures contributes to the next phase of the method serving as the output options. To each measure, factors, weights, and sustainability impacts were assigned. The connection of the phases was realized by creating the utility value of the measure, which enables the ranking of the measures. The approach provides a comprehensive framework of connecting employee requirements, employer willingness, and site-specific opportunities by defining a quantitative utility function, which results in a list of most suitable measures for a specific workplace.

Sustainable urban mobility: One definition, different stakeholders’ opinions

During recent decades, the debate on how to sustain urban mobility has accelerated. Transport decision-making has been more reflective of sustainability issues and quality of life in cities; this process has especially accelerated with the setting of modern urban mobility planning concepts, substantially focusing on transport demand regulation and management, and Sustainable Urban Mobility Plans taking over the role of cities’ primary transport-related strategic documents. The approaches to sustainable mobility visioning and planning differ within cities, as do their key stakeholders’ opinions. Our research aims to reveal the main shared viewpoints on the preferred paths towards sustainable urban mobility. We use the Q method, which is a statistical method to study subjectivity. It enables determination of characteristic shared viewpoints on a particular subject. A qualitative analysis assessing subjective opinions is combined with the quantitative approach of a factor analysis of statements ranked by respondents. The study was undertaken on 36 carefully selected stakeholders situated within the Czech Republic. Our results indicate that even stakeholders sharing the same definition of sustainable urban mobility may substantially differ in regard to their ideas on how to achieve it. Furthermore, we show that some of the attitudes expressed and measures appreciated by our respondents would not be recognised as sustainable by environmentalists. There is still a wide gap between sustainable mobility theory and its implementation in practice. All these findings represent barriers to sustainable mobility development in urban areas. Therefore, the paper also conveys relevant policy implications.

Interpretations of Downs–Thomson paradox with median bus lane operations

The Seoul Metropolitan Government has implemented a public transportation priority policy with the introduction of a 121.1 km long of the Median Bus Lane (MBL). Therefore, to analyze the impacts of the introduction of Jongro-gu MBL in January 2018, an effect analysis using the speed and volume of passenger cars and buses in Seoul is conducted in this study. The interpretation of the Downs-Thomson (D-T) paradox demonstrates that the reduction in passenger car mode capacity can increase total social benefits. This study shows that the speeds of passenger cars and buses have reached the deterministic user equilibrium after the MBL introduction, resulting in an increase in total social benefits. Moreover, it demonstrates that the bus priority policies, which gives the right to keep the capacity of buses guaranteed in urban areas, will be effective. The interpretation of the D-T paradox will contribute to the formulation of transport demand management policies.

Exploring planning tribunal decisions on travel plans for new developments

Travel plans, also known as transportation demand management (TDM) plans, can be required through the planning approval process for new developments. Where decisions on development applications are appealed, a planning tribunal may review the decision, including the requirement for a travel plan. This research aimed to explore planning tribunal decisions on travel plans for new developments. Using a case study of Victoria, Australia, an analysis of 178 planning tribunal reports from 2005–16 showed that travel plan requirements were accepted in 88% of cases. Larger planning tribunal groups were found to be associated with higher acceptance of travel plan requirements. Travel plan requirements with more prescriptive/tailored wording were over 20 times more likely to be accepted compared to those with generic wording. Recommendations for improving practice include the development of more clearly worded travel plan conditions, provision of training programs and guidelines, and the introduction of supportive planning policy.

Modelling heterogeneity in behavioral response to peak-avoidance policy utilizing naturalistic data of Beijing subway travelers

Studies of travelers’ response behavior to transportation demand management is receiving substantial attention among researchers and transport operators in recent years. While previous studies in this area have generally assumed that the sensitivity of travelers to different factors is homogeneous and relies on survey responses, which may be prone to self-reporting errors and/or subject to behavioral incongruence. Relying on naturalistic data, this paper aims to investigate the behavioral response to pre-peak discount pricing strategy in the context of the Beijing subway with a special focus on the heterogeneity among the travelers. Anonymous smart card data from 5946 travelers before and after the introduction of a peak avoidance policy in Beijing are used to construct a latent class choice model to capture the sensitivity to different factors and the associated taste heterogeneity of travelers. Given the passive nature of the data, the model can offer more realistic outputs. The results indicate that there is substantial heterogeneity in travelers’ responses to the peak avoidance policy, and that they can be probabilistically allocated to four latent classes. For all classes of travelers, the decision to shift their departure to off-peak is affected by the monetary saving, the required change in departure time and the frequency of travel, but in different magnitudes. In particular, only two classes of travelers (who exhibit lower standard-deviation in pre-intervention departure time) show significant sensitivity to price changes indicating that the discount policies are more likely to be effective for these groups. The rest of travelers are largely price insensitive – warranting the need for non-monetary incentives as opposed to fare discounts. To the best of our knowledge, this study is the first to innovatively apply the LCC framework to analyze travelers’ heterogeneous behavior using large-scale smart card data without socio-demographic information. The findings can provide guidance to the subway authority in devising differential peak avoidance policies targeted for different groups of users, which are likely to be more effective than the current ‘one size fits all’ approach.

Transportation demand management (TDM) and social justice: A case study of differential impacts of TDM strategies on various income groups

The paper presents an evaluation of welfare gains and losses of individual commuters in response to various transportation demand management (TDM) strategies. A computationally tractable mode choice model for commuters is estimated using data collected in the Region of Peel, Ontario in 2014 through a joint revealed and stated preference survey. The survey intended to obtain information on the impacts of TDM strategies on commuting. The empirical investigation reveals that the impact of TDM strategies vary by different income groups. In particular, low-income households are the clear gainers when transit attributes such as fare and travel time are reduced, and vice versa. It is found that bike-related TDM strategies, such as showers, lockers, and bike accessible ramps, can certainly help encourage the use of bikes among commuters, but on their own, their impact will most likely be insufficient to convince commuters to change their commuting mode. However, bike share, car share, and emergency ride home programs at workplaces have higher compensating variation than the other TDM strategies. The likely cause of their higher impact could be due to the low entry investment required, in the case of the bike and car share programs, and the risk remediation provided, in the case of emergency ride programs. Though this microeconomic approach is explicitly aimed at the commuters of the Region of Peel, a similar approach can be taken elsewhere to measure the differential monetary benefit of TDM strategies on different income groups across the population.

Modeling of incentive-based policies for demand management for the Tehran subway

Issues such as the increasing demand for intra-city trips in megalopolises, noticeable growth in the use of public transportation, problems caused during peak traffic hours, and limitations related to the augmentation of transport fleet sizes all underscore the necessity of transportation demand management. In this study, we sought to address the question of whether incentive-based plans can alter passengers’ behavior during peak hours. To accomplish the task, we used 432 Tehran subway system passengers’ stated preferences. The results indicate that incentives, such as ticket discounting during off-peak hours, increasing availability of seats, and offering free Wi-Fi and breakfast discount coupons, can be influential in altering passenger behavior during peak hours. In addition, wait time reductions during off-peak hours can be an incentive for changing the travel behavior.