Transportation Demand Management Strategies Research Articles

NEW URBANISM IN CONTEXT TO MOBILITY

In recent years, the concept of New Urbanism has gained significant attention as a means of addressing the challenges of urban development and mobility. There has been a growing interest in understanding how new urbanism principles impact mobility patterns and behaviours. The New Urbanism, initially conceived as an anti – sprawl reform movement, New Urbanism is an urban planning and design approach that emphasizes walkability, mixed land uses, and connectivity. In new urbanism, there is a strong emphasis on promoting alternative modes of transportation to reduce car dependency and create more sustainable and liveable communities. These abstract aims to explore the relationship between new urbanism and mobility, focusing on the various factors that influence transportation. This paper aims to explore the key principles of New Urbanism that enhance mobility and examine the ways in which it promotes the use of alternative modes of transportation. Through this research, we hope to provide insights into the potential benefits and challenges of New Urbanism as a means of improving mobility in urban areas. Some common modes of transportation in new urbanist developments include walking, cycling, and public transit by examining the role of street design, infrastructure, and transportation demand management strategies. This not only helps to reduce traffic congestion and air pollution but also promotes a healthier and more active lifestyle. The goal is to encourage alternative transportation options. This research seeks to provide insights into creating more sustainable and accessible urban environments. This research will contribute to the ongoing dialogue on how to design cities that prioritize mobility, promote alternative transportation options, and reduce car dependency. Key Words: New urbanism, Sustainable mobility, Transit-oriented development (TOD), Walkable communities, Active transportation, public space, public transit.

Smartphone mobility assistants. A lever to guide route choice preferences in mass transit?

The regulation of passenger congestion in mass transit is a persistent issue that requires ingenious and cost-effective solutions to ensure that related operations run at optimum capacity. Mass transit operators may implement Public Transport Demand Management (PTDM) strategies like cognitive levers to tackle this issue by targeting passengers’ behaviour during the route planning that precedes travel in mass transit. In this regard, multiple experimental studies in cognitive psychology and sciences have shown that transit maps can be used to guide passengers’ route choice preferences in mass transit.A route choice experiment was conducted to examine the potential of smartphone mobility assistants as a tool to guide mass transit users’ away from the fastest options that tend to be predominantly preferred. 582 participants took part in an online study where they engaged in a route selection task by indicating their route choices in the Île-de-France mass transit system. We measured how participants’ preferences for the fastest route varied depending on the visual format in which routes were presented (on a transit map, on a timeline or listed briefly as per current trends), the presence of conflicting visuo-spatial information on the transit map (fastest choice = shortest vs. longest choice) and the level of comfort (availability of simple transfers and/or less congested routes).The main results suggest the existence of two levers than can be implemented in smartphone mobility assistants to manage passenger congestion in mass transit: (1) a perceptive heuristic whereby passengers presented with a transit map manifest a preference for the route presented as the shortest on the map and (2) the possibility to sway a proportion of passengers away from the choice of the fastest route by presenting information about the comfort levels of alternative options.

Transport Demand Management Strategy Priority Assessment Based on Expert Judgment

The main problem of transportation is the very high growth of vehicles causing congestion, resulting in various derivative impacts such as pollution, fuel waste, time value, and other environmental problems. This problem can be solved by Transportation Demand Management (TDM). TDM is a combination of various strategies, which strategy should be chosen whose priority depends on the conditions of each region. This research was conducted in a medium-scale city by determining the priority of TDM using the Analytical Hierarchy Process (AHP) tool. The final result of the judgment is the priority weight of the TDM strategy that will be applied with a CR value of < 10%, namely the pull strategy. This strategy is represented by improving public transport services and infrastructure (especially the integration of public transport services). This study shows that the strategy group with a high AHP Consensus Index (ACI) score means a high consensus among experts.

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.

Measuring the Impacts of Employer-based Transportation Demand Management Programs on an Interstate Corridor

This article is based on a study that analyzed the impacts of employer-based transportation demand management (TDM) strategies and the performance of a transportation system. The study simulated the effects of the Washington State Commute Trip Reduction (CTR) programs implemented by 189 employers on an 8.6-mile segment of I-5 in the Seattle downtown area. This comprehensive dataset included before/after employer trip reduction plans and the origins/destinations of nearly 63,000 commuters working for those 189 employers. The current performance of the selected network was compared to that of a scenario with reduced vehicle trips due to CTR programs added back onto the network. The hypothesis of the research was that a widescale adoption of employer-based strategies is likely to have a measurable difference on the performance of a transportation system. The analysis found the reduction in AM peak delay due to TDM programs implemented in the study area was 152,489 vehicle-minutes and 17,297 vehicle-miles of travel. Savings in PM-peak delay were 169,486 vehicle-minutes and 14,510 vehicle-miles were reduced. Fuel saved in the AM and PM peaks were 3,489 and 4,314 gallons, respectively. Some segments and periods saw significant improvement. The study proved that comprehensive TDM programs could have a significant impact on the operation of a major Interstate corridor.

Effectiveness of Free Bikes and E-Bikes for Commute Mode Shift: The Case of Google’s Lending Program

In 2015, Google began a new transportation demand management program designed to increase bike commuting to their two main corporate campuses in Mountain View and Sunnyvale, CA, United States by lending conventional and electric assisted bikes to employees at no cost to them. Following the lending period, Google incentivized bike purchases, among many other program co-benefits to increase bike commuting. Using a series of bivariate and multivariable analyses, we estimate the program led to average bike commute increases of approximately 1.7–2.3 days per week, roughly a tripling of prior bike commute rates for participating employees. After the program, bike rates of participants diminished slightly, but were still greater than baseline (increase of 1.3–1.9 days per week). Furthermore, nearly all the increases in bicycling are likely attributed to decreases in single occupancy vehicle (SOV) commuting. This study offers a first look at the potential for bike lending as a transportation demand management strategy for large employers in suburban settings which can help other employers design their own programs.

To go, or not to go? Modelling the effects of employment decentralisation on telecommuting preferences

Employment decentralisation and telecommuting are both gaining popularity as urban and transport demand management strategies. However, as employment shifts to new economic centres that are developed nearer homes, the disamenities related to commuting to the Central Business District (CBD) become less relevant. This potentially induces existing telecommuters to instead travel to their workplaces, influencing the extent to which telecommuting minimises commuting. This study aims to establish a framework to model the effects of employment decentralisation on existing telecommuters by modelling their commute preferences based on two inductive effects on travel: reduction of commute times due to nearer workplaces and reduced crowdedness due to the dispersion of unidirectional CBD-bound travels. A structural equation model was developed from a stated preference survey depicting various workplace locations, crowdedness levels and commute times among 574 employees in Singapore who commute using the Mass Rapid Transit (MRT) system. Commute preferences among telecommuters who switch to physical commutes could be attributed to these inductive effects. Among these effects, reduced crowdedness is more likely to cause the shifts away from telecommuting, more so than reduced commute times. The results also demonstrate that these effects are in turn influenced by the benefits of commuting enhanced by employment decentralisation. The significance of sociodemographic factors to these benefits is investigated as relevant practical implications surface regarding such decentralisation policies.

Tradable parking permits as a transportation demand management strategy: A behavioural investigation

Tradable parking permit schemes can be an effective transportation demand management strategy for cities where cars are viewed as a status symbol and wide adoption of shared transportation modes face resistance. We investigate the feasibility and effectiveness of tradable parking permit schemes in high density cities with parking constraints. We focus on the effect of four behavioural factors, namely, reference dependence, endowment effect, social nudge, and environmental consciousness. Online experiments are conducted via Amazon Turk Prime platform, with participants recruited from large cities in China. Our investigation on reference dependence and endowment effect suggest that tradable parking permit schemes is feasible and efficient. Environmental consciousness can improve the schemes' effectiveness in encouraging participants to switch to public transport, but social nudge shows no significant effect. Our study provides much-needed empirical findings about tradable credits for transportation management on the individual level.

Mobility-as-a-Service as a transport demand management tool: A case study among employees in the Netherlands

This paper examines the potential role of Mobility-as-a-Service (MaaS) as a transport demand management (TDM) tool to influence commuting mode choice. A stated choice experiment was conducted among employees in the Netherlands to capture the effect of integrating TDM measures with MaaS on mode choice behavior. We found that the train and car-sharing attributes’ variations are influential factors on employees’ mode choice behavior. Moreover, the mobility package price played a significant role in mode choice even though most employees in the Netherlands receive a full or partial travel allowance from their employers. Similarly, the increase in parking tariffs was an influential measure for car users who used street/garage parking spaces. However, the effects were not equal for all respondents. Young, low-income and multi-modal commuters are more susceptible to travel behavior changes than old, high-income, and car-dependent employees. In conclusion, MaaS could be seen as a promising element in TDM strategies combining carrots (enablers) and sticks (deterrents), albeit for specific groups users.

Managing campus parking demand through course scheduling – an approach to campus sustainability

Purpose The purpose of this study is to use the optimization modeling method to explore whether there is an ideal arrangement of course enrollments that can yield optimal parking demand and supply on college campuses. Design/methodology/approach Using the University of Louisville as a case study, this study deploys a three-step analytical process to examine the correlation between parking demand and course enrollment, estimate parking demand based on course enrollment with regression analyses and embed this estimated relationship in an optimization model that minimizes on-campus parking demand and supply. Findings The correlation analyses suggest significant correlations between course enrollments and on-campus parking. The correlation patterns are different between students and university employees. The optimization results indicate that coupling parking supply and course scheduling decisions can reduce parking supply by 30%. Originality/value Voluminous studies on sustainable campus transportation have focused on transportation demand management strategies. The relationship between course-scheduling and parking demand was not explicitly accounted for in most studies. This study's results reveal that parking demand on campus depends on the number of courses offered across time. Thus, factoring and optimizing course schedules in campus parking decisions remains a viable and essential option to reduce on-campus parking demand.

Development of households’ vehicle choice models for travel accompanying arrangements

This study examines the vehicle choice behavior of multicar households for different travel accompanying arrangements. It investigates the influence of various travel attributes and neighborhood characteristics to understand how individuals in a multicar household make a choice of vehicle for a specific travel arrangement. The study considers three types of travel arrangements based on accompanying persons (i.e. alone, with household members and with non-household members) and categorizes households’ vehicle fleet into six classes: subcompact, compact, midsize, SUV, vans/minivans/pickup-trucks and luxury vehicles. Three flexible latent class logit models (LCLMs) are developed in this study to estimate vehicle choice decisions by utilizing data from the ‘2015 Nova Scotia Travel Activity (NovaTRAC) survey’. Results suggest that individuals tend to prefer subcompact vehicles and SUVs for longer travel time while traveling alone and with non-household members, respectively. They show higher likelihood to choose midsize vehicles for long-distance trips when traveling with household members. The behavioral models also capture unobserved heterogeneity across population by developing flexible class allocation models within LCLM frameworks that probabilistically distribute individuals into discrete latent classes based on their socio-demographic characteristics. Model results intuitively suggest two latent classes for all three travel arrangements and explore heterogeneity across classes. Results of this study will assist the transportation planners and policymakers to develop alternative transportation demand management strategies that aim at reducing vehicular emission and energy in a traffic network.

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.

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.

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.

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.

Strategic Development of Transportation Demand Management in Jordan

The urban population growth and increase in a number of vehicles have affected the travel demand on Jordan Streets. The study aims to apply the Transportation Demand Management (TDM) policies to reduce the traffic flow in urban areas and improve the Level of Service (LOS). A group of a combination between TDM and Transportation System Management (TSM) strategies have been conducted, when TDM strategies were not successful to improve LOS. TSM concept refers to any group of actions that increase the capacity of roads network. Synchro 8 software was used to analyse the intersection conditions as important point, connecting two main arterial street in Amman area. A significant reduction in delay and fuel consumption was measured, but there was no real enhancement in LOS. The LOS was improved, when traffic demand was reduced by 20% and capacity was increased with 6 lanes on each approach for the existing conditions. The percentage of saving in fuel consumption and delay was observed to be around 64% and 63%, respectively for the next 5 years.

Tradable Parking Permits as a Transportation Demand Management Strategy: A Behavioural Investigation

Tradable parking permit schemes can be an effective transportation demand management strategy by allocating parking spaces efficiently and encouraging the use of non-single occupancy modes of transport. We investigate the feasibility and effectiveness of tradable parking permit schemes in high density cities with parking constraints. We focus on the effect of four behavioural factors, namely, reference dependence, endowment effect, social nudge, and environmental consciousness. Online experiments are conducted via Amazon Turk Prime platform, with participants recruited from eight Chinese cities. Our investigation on reference dependence and endowment effect suggest that tradable parking permit schemes is feasible and efficient. Environmental consciousness can improve the schemes’ effectiveness in encouraging participants to switch to public transport, but social nudge shows no significant effect. Our study provides much-needed empirical findings about tradable credits for transportation management on the individual level.

Analysis of a joint entry- and distance-based cordon pricing scheme: a dynamic modeling approach

Transportation demand management (TDM) covers strategies for reducing traffic congestion within the affected urban areas. Congestion pricing includes a branch of TDM strategies; among them, the entry-based cordon pricing, i.e., applying charge on entry, is the most popular because of practicality and social acceptance. Many researchers have investigated different second-best approaches for evaluations of cordon pricing plans, mostly by applying static traffic assignment methods. In this paper, a joint entry- and distance-based scheme is proposed to circumvent the deficiencies intrinsic to each. The optimal joint design is considered as the solution to an optimization problem, in which an equilibrium dynamic traffic assignment model is used to take account of flow variations and represent congestion effects more realistically. The problem is solved for the network of Sioux Falls by using an enumeration algorithm, and the solution is compared with those obtained for distinct entry- and distance-based schemes. Based on the results, the joint tolling has the best performance in reducing the total travel time of the travelers and in alleviating the congestion level inside the cordoned area, while generating a higher level of revenue from tolls. Furthermore, the numerical experiments show the unreliability of the results by static against dynamic modeling approach.

New and Unique Aspects of University Campus Transportation Data to Improve Planning Methods

University campuses have unique characteristics that influence travel demand. They benefit from controlled planning frameworks, can influence almost all aspects of pricing and on-campus transportation infrastructure, benefit from simplified many-to-one travel patterns, and maintain extensive data on nearly the entire campus community. Campuses represent millions of trips daily, yet little research has been conducted that focuses on developing tailored frameworks to assess transportation demand, target travel demand management strategies, or to assess environmental or other impacts. This paper describes how two sets of commonly collected data can be leveraged to provide new insights into travel behavior, incentives, and environmental policy. Specifically, we illustrate the fusion of standard travel demand survey data with disaggregate and precise household address data to provide new inferences into appropriate strategies to improve transportation sustainability. We applied three use-cases—carpool potential, walk and bike incentives, and mode shift to reduce greenhouse gas emissions—to the data from three universities, University of Tennessee, Cal Poly San Luis Obispo, and San Diego State University, respectively. We found that the richer dataset provided substantially better data resolution that allowed for transportation strategies to be more precisely targeted and expansion of the potential impact of transportation demand management strategies.