Travel Demand Management Strategies Research Articles

Parking Assessment in the Context of Growing Construction Activity and Infrastructure Changes: Simulation of Impact Scenarios

This paper investigates the impact of construction activity and associated parking on the overall parking behaviour of a rapidly changing metropolitan city. The study adopted the modelling and simulation of baseline and impact scenarios of increasing demand, changing parking infrastructure and limited on-street parking supply. Results indicate that construction and population growth, as well as limited parking capacities, will have a significant impact on overall parking behaviour. Furthermore, the proximity to the construction site was identified as an important consideration in carpark location choice. These findings suggest that decisions on capacity and locations should be based on critical factors, including the behaviour of various users and loads. This study has implications for policymakers on the provision and selection of parking infrastructure for a range of users. Outcomes of this research have directly influenced the planning framework for the Parramatta Light Rail project, future zoning, and infrastructure development in Parramatta. These findings can be used for recommendations on developing travel demand management (TDM) strategies for various stakeholders, depending on the context of parking demand, supply, and future changes. However, the study is limited to surveys of actual parking demand without considering latent demand during the peak period of the day.

Commuting service platform: Concept and analysis

We propose and investigate the concept of commuting service platform (CSP) that leverages emerging mobility services for commuting and connect directly commuters (employees) and their worksites (employers). By applying the two-sided market analysis framework, we show under what conditions a CSP may present the two-sidedness. Both the monopoly and duopoly CSPs are then analyzed. We show how the price allocation, i.e., the prices charged to commuters and worksites, can impact the participation and profit of the CSPs. We also extend the method to consider locations of homes and worksites, and propose a general modeling framework to consider the locations. Discussions are presented on how the findings in this paper may help build CSP in practice and how to develop new, CSP-based travel demand management strategies.

Cordon Pricing, Daily Activity Pattern, and Exposure to Traffic-Related Air Pollution: A Case Study of New York City

Road pricing is advocated as an effective travel demand management strategy to alleviate traffic congestion and improve environmental conditions. This paper analyzes the impacts of cordon pricing on the population’s daily activity pattern and their exposure to particulate matter by integrating activity-based models with air quality and exposure models in the case of New York City. To estimate changes in public exposure under cordon pricing scenarios, we take a sample of employees and study their mobility behavior during the day, which is mainly attributed to the location of the work and the time spent at work. The selection of employees and their exposure during the duration of their work is due to the unavailability of exact activity patterns for each individual. We show that the Central Business District (CBD) experiences a high concentration of PM2.5 emissions. Results indicate that implementing cordon pricing scenarios can reduce the population-weighted mean of exposure to PM2.5 emissions by 7% to 13% for our sample and, in particular, by 22% to 28% for those who work in the CBD. Furthermore, using an experimental model and assuming constant conditions, we point out the positive influence on indoor exposure for two locations inside and outside the CBD in response to cordon pricing. Considering the correlation between long-term exposure to fine particulate matter and the risks of developing cardiovascular disease and lung cancer, our findings suggest that improved public health conditions could be provided by implementing cordon pricing in the New York City CBD.

A Systematic Review of the Role of Road Network Pricing in Shaping Sustainable Cities: Lessons Learned and Opportunities for a Post-Pandemic World

The growing number of studies on road network pricing requires adoption of systematic methodologies to assess research outcomes and provide an unbiased summary of research findings and lessons learned. This paper aims to identify and analyse primary studies related to a set of research questions on road network pricing that primarily address the effectiveness of road network pricing as a travel demand management strategy. The paper achieves this by consolidating the fragmented evidence on the topic and identifying the role of transport pricing in steering our post-pandemic cities on a path of sustainable urban mobility. The paper uses a reliable and auditable systematic approach to examine past and current research trends, resulting in a rational assessment of the role and impacts of road network pricing as a travel demand management strategy. The paper achieves this by performing a bibliometric citation analysis that identifies 105 articles of valuable research contributions that represent fundamental knowledge in the development of research covering the period between 2007 and 2020. Importantly, the review identifies four main research themes in the literature, namely implementation impacts, innovations in technology, acceptability, and modelling methodologies for determining impacts, that are core elements of the research effort on the travel demand management and sustainability aspects of road pricing. Inductive reasoning is then used to address emerging issues, applications, and the effects of road network pricing in reducing congestion and enhancing urban centre environmental quality. The paper concludes with a discussion of policy directions for overcoming barriers to the implementation of road network pricing as an effective strategy for addressing modern-day urban mobility challenges such as rising urban populations, emissions, and pollution particularly amid and post COVID-19. Finally, the paper provides a roadmap of future research opportunities that can heighten the role of road network pricing in shaping the directions of sustainable urban transport policies and strategies.

Validation of an activity-based travel demand modeling system

ABSTRACT This paper presents the validation analysis of an activity-based travel demand modeling system – shorter-term decisions simulator (SDS). It compares microsimulation results of SDS with observed census and travel-activity survey data for the base year 2006, and forecasting years – 2016 and 2018. Base year validation suggests SDS’s reasonable capability to replicate observed data as indicated by lower absolute percentage error (APE) measures. SDS exhibits satisfactory performance for the forecasting years as well. For instance, Kolmogorov–Smirnov (KS) tests during the activity start time and duration analysis for year 2018 yield smaller D-values than critical values, which indicates a better fit model. Prediction of tour-level mode choice, shared travel choice, and vehicle allocation also exhibit good accuracy (± 4%). Validation results presented in this study demonstrate SDS’s promising forecasting capability so that the model can be utilized efficiently to develop and test alternative land use and travel demand management strategies.

What Factors Affect Commuters’ Utility of Choosing Mobility as a Service? An Empirical Evidence from Seoul

Mobility as a Service (MaaS), which integrates public and shared transportation into a single service, is drawing attention as a travel demand management strategy aimed at reducing automobile dependency and encouraging public transit. In particular, there have been few studies that recognize traffic congestion during peak hours and identify related factors for practical application. The purpose of this study is to explore what factors affect Seoul commuters’ mode choice including MaaS. A web-based survey that 161 commuters participated in was conducted to collect information about personal, household, and travel attributes, together with their mode preference for MaaS. A latent class model was developed to classify unobserved latent groups based on trip frequency by means and to identify factors influencing mode-specific utilities (in particular, MaaS service) for each class. The result shows that latent classes are divided into two groups (public transit-oriented commuters and balanced mode commuters). Most variables have significant impacts on choice for MaaS. The coefficient of MaaS choice of Class 1 and Class 2 were different. These findings suggest there is a difference between the classes according to trip frequency by means as an influencing factor in MaaS choice.

Forecasting with a joint mode/time-of-day choice model based on combined RP and SC data

The factors influencing trip departure time are taking more importance in urban planning practice since congestion is increasingly being addressed by travel demand management (TDM) strategies. In this paper we formulate and estimate a joint travel mode-departure time model for commuting trips using combined revealed preference (RP) and stated choice (SC) data. The RP data considered nine alternative modes and up to 11 time periods, and the level-of-service data were obtained at an unusual level of precision using GPS measurements. The travel time, cost and cost divided by the wage rate coefficients were fairly similar in both the RP and SC environments, suggesting equal error variances for both datasets. The only parameters that differed between each type of data were those associated with the schedule delay early (SDE) and late (SDL) variables required by Small’s Scheduling Model. This may be due to the potentially different temporal perspectives between RP choices (longer term decisions) and SC decisions, arguably shorter term given the nature of the experiment and the context presented in it (implementation of a congestion charging policy and a flexible working-hours scheme). The models were used to forecast the impacts of a hypothetic congestion charging scheme in Santiago, showing that the schedule delay coefficients derived from the SC context produced a smoother and less-peaked temporal distribution of travel demand than the RP parameters.

Optimizing Budget Allocation for Incentive-Based Active Travel Demand Management Solutions

Incentive-based travel demand management (IBTDM) strategies utilize rewards to redistribute travel demand across space and time. Such congestion-alleviation solutions are usually managed by small private companies with constraint budgets. Aside from spending money on incentives, running promotional campaigns to achieve the gains in market share is essential for maintaining the financial health of IBTDM programs. Therefore, the budget allocation between the two counterparts—incentive and marketing expenditure—needs to be wisely determined. Based on the bottleneck model, this paper proposes an optimal budget allocation scheme considering the impact of a budget constraint and market penetration. It was found that the constraint budget should be prioritized to attract those with lower marketing costs in general. In situations with an insufficient budget and when marketing costs were lower for attracting lower-income individuals, IBTDM decision-makers should focus on those lower-income individuals at first. This mitigates inequity issues to some extent. Therefore, policy makers or planners should pay more attention to marketing cost when developing a marketing plan and try to reduce marketing cost to make full use of incentive budget.

Potential of telecommuting for different employees in the Indian context beyond COVID-19 lockdown

During the last decade, telecommuting has become quite popular whereas, in developing countries like India, this phenomenon is limited to a select group of employees due to various factors. The COVID-19 pandemic, the nationwide lockdown and the subsequent new normal scenario, has made the adoption of telecommuting by different type of employees indispensable thus giving us the opportunity to conduct empirical research towards understanding the factors influencing telecommuting adoption, productivity and activity-travel behaviour during telecommuting in India. The data used in this survey has been collected in two phases to capture the evolving situation of the pandemic. The analysis has been done based on revealed preference data considering three time periods i.e., before COVID-19, during COVID-19 nationwide lockdown, after the termination of the lockdown (after first wave) and for the post-COVID 19 scenario where stated preferences are considered. In the first step, telecommuting behaviour, activity-travel pattern during telecommuting and productivity is estimated based on aggregate sample data for various socio-economic groups for the period before and during COVID-19 (1. during lockdown 2. during new normal). Next, factors influencing telecommuting adoption and productivity are determined which has been used in our stated preference survey conducted in the second phase. A logistic regression model has been developed to test the association of existing telecommuting behaviour, activity-travel pattern, factors influencing productivity, socio-economic characteristics and occupation categories with future telecommuting adoption. The findings of this research can not only provide insights to urban planners and policymakers to design sustainable travel demand management strategies but can also help employers to design appropriate telecommuting strategies at the organization level which will help to attain the desired productivity levels. Our empirical analysis reveal two major findings, i.e., a large percentage of employees can achieve their desired productivity by working from home and the ‘rebound’ effect as identified in literature seems to have little impact in the Indian context. The novelty of this research lies in the comprehension of the adoption process, and the behavioural analysis including adoption, productivity, activity, and travel of telecommuters in the context of a developing country for the first time.

Incorporating Travel Time Reliability in Equitable Congestion Pricing Schemes for Heterogeneous Users and Bimodal Networks

Congestion pricing is proposed as an effective travel demand management strategy to circumvent the problem of congestion and generate revenue to finance developmental projects. There are several studies focusing on optimal pricing strategies to minimize the congestion level or maximize the revenue of the system. However, with regard to equity issues, benefiting only users with higher value of time is claimed to be the main factor that prevents implementation of such policies in practice. While many studies aimed to tackle the equity issues by certain welfare analyses, most of these studies fail to fully consider realistic features of users’ behavior and the uncertainty in link travel times. Given the variability of travel time in real-world networks and the impacts of pricing policies on path travel time distributions, it is important to consider the users’ reliability valuations, in addition to their travel time valuations. Thus, the goal in this study is to find an equitable pricing scheme that minimizes the total travel time of auto users in a general bimodal network considering heterogeneous users with different values of time and reliability. A particle swarm optimization algorithm is proposed to find self-funded and Pareto-improving optimal toll values. A reliability-based user equilibrium algorithm is embedded into this optimization algorithm to assign travelers to the equilibrated paths for different user classes given toll values. The proposed approach is successfully applied to a modified Sioux Falls network to explore impacts of subsidization, congestion level, and considering travel time reliability on the pricing strategy and its effectiveness.

The Interaction between E-Shopping and Shopping Trips: An Analysis with 2017 NHTS

Advances in information and communication technologies (ICT) have dramatically changed the nature of shopping and the way people travel. As this technology becomes deeply rooted in people’s lives, understanding the interplay between this way and personal travel is becoming increasingly important for planners. Using travel diary data from the 2017 National Household Travel Survey (NHTS) data for structural equation modeling (SEM) analysis, it revealed the interaction between e-shopping and shopping trips and the factors that affect this bidirectional relationship. Results show that e-shopping motivates shopping trips, and in-store shopping inhibits online shopping. It can be obtained that the increase of one standard deviation of e-shopping will increase the shopping trip by 0.17 standard deviation. When shopping trips increase by one standard deviation, e-shopping behavior also decreases by 0.12 standard deviation. The results also demonstrated that e-shopping and shopping travel behavior is heterogeneous across a variety of exogenous factors such as personal attributes, household characteristics, geography, travel distance/duration, and travel mode. Identifying the interaction may help formulate better transportation policies and lay the foundation for travel demand management strategies to reduce the stress on the transportation system and meet individual travel needs.

Understanding the effects of travel demand management on metro commuters’ behavioural loyalty: a hybrid choice modelling approach

As part of efforts to promote sustainable mobility, many cities are currently experiencing the rapid expansion of their metro network. The consequent growth in ridership motivates a broad range of travel demand management (TDM) policies, both in terms of passenger flow control and dynamic pricing strategies. This work aims to reveal the impact of TDM on metro commuters’ behavioural loyalty using stated-preference data collected in Guangzhou, China. Commuters’ behavioural response to TDM strategies is investigated in terms of the possible shift in departure time and travel mode. A hybrid choice model framework is used to incorporate four latent variables of interest, i.e., service quality, overall impression, external attractiveness and switching cost, into the discrete choice model and thereby capture the relationships between the attitudinal factors and observed variables. The model estimation results indicate that the four latent variables all prove useful in interpreting commuters’ behavioural loyalty. Commuters’ perceived service quality and overall impression both show a positive effect on their willingness to continue travelling by metro and are thus instructive for ridership retention. External attractiveness is found to be significant only in the case of the tendency to shift to a private car. Switching costs reveal commuters’ emotional attachment to their already developed commuting habit. These insights into commuters’ behavioural change intention enable metro operators to enhance commuters’ loyalty to their service and develop more effective TDM strategies in future practice.

The fuzzy modelling of personal vehicle usage in Isfahan: Quantifying contributions from different travel Demand management strategies

Modifying travel behavior is a pressing issue in the face of increasing urbanization and the challenges that poses for existing infrastructure and deteriorating environmental conditions. Such challenges are particularly acute in cities which have a rich history and which have seen substantial population growth. One such is Isfahan, the third largest city in Iran, where the current study was conducted. Successful modification of travel behavior relies on an understanding of that behavior, but there are substantial methodological and theoretical barriers to doing so. This study reports a survey (N = 400) of Isfahan residents and visitors, and the modeling of the resulting data using a fuzzy logic approach. Together they illustrate the relative desirability, and potential effectiveness, of different Travel Demand Management strategies that might be employed to address the traffic challenges of Isfahan and other heritage cities worldwide.

Linking Residential Parking to Automobile Transportation Impact Outcomes at a Development Level

Although there exists a well-studied relationship between parking policies and automobile demand, conventional practices evaluating the transportation impacts of new land development tend to ignore this. In this paper, we: (a) explore literature linking parking policies and vehicle use (including vehicle trip generation, vehicle miles traveled [VMT], and trip length) through the lens of development-level evaluations (e.g., transportation impact analyses [TIA]); (b) develop a conceptual map linking development-level parking characteristics and vehicle use outcomes based on previously supported theory and frameworks; and (c) evaluate and discuss the conventional approach to identify the steps needed to operationalize this link, specifically for residential development. Our findings indicate a significant and noteworthy dearth of studies incorporating parking constraints into travel behavior studies—including, but not limited to: parking supply, costs or pricing, and travel demand management strategies such as the impacts of (un)bundled parking in housing costs. Disregarding parking in TIAs ignores a significant indicator in automobile use. Further, unconstrained parking may encourage increases in car ownership, vehicle trips, and VMT in areas with robust alternative-mode networks and accessibility, thus creating greater demand for vehicle travel than would otherwise occur. The conceptual map offers a means for operationalizing the links between: the built environment; socio-economic and demographic characteristics; fixed and variable travel costs; and vehicle use. Implications for practice and future research are explored.

Promoting Autonomous Vehicles Using Travel Demand and Lane Management Strategies

A key challenge facing cities of today is the persistent and growing urban congestion that has significant adverse effects on economic productivity, emissions, driver frustration, and quality of life. The concept of smart cities, which can revolutionize the management of metropolitan transportation operations and infrastructure, shows great promise in mitigating this problem. Specifically, the automation and connectedness (A&C) of smart city entities such as its infrastructure, services, and vehicles, can be helpful. In this regard, this paper focuses on the potential of autonomous vehicles (AVs) and AV infrastructure, particularly during prospective transition era where there will be mixed streams of AVs and human driven vehicles (HDVs). The paper considers two aspects of this potential: connectivity-enabled travel demand management and travel infrastructure supply through lane management. To demonstrate the opportunity associated with this potential, this paper first presents an AV-enabled tradable credit scheme (TCS) to manage travel demand. Here, the transportation authority distributes travel credits to travelers directly and instantaneously using the AV’s A&C features. Then, travelers use their A&C features to pay these credits for travel at specific locations or times-of-day according to their choices of lane types and links. With regard to supply, the paper considers that the road network consists of two lane types: AV-dedicated, and mixed traffic lanes, and develops a scheme for Travel Demand and Lane Management Strategies in AV transition era (TLMAV). First, the paper models the expected travel choices based on the user equilibrium concepts, at different levels of AV market penetration. Then, the existence of the optimal solution in terms of link flows and the prevailing travel credit price is demonstrated. Then, the paper establishes the optimal TLMAV that minimize total travel time subject to user equity constraints. The results demonstrate the extent to which HDV users suffer increase in travel cost if equity is not considered in the model. The results also show how the transportation agency can use TLMAV to keep HDV travel costs to acceptable levels, particularly during early periods of the AV transition period.

An innovative GPS smartphone based strategy for university mobility management: A case study at the University of RomaTre, Italy

Private car dependence has adverse effects on both mental and physical health. Travel Demand Management (TDM) strategies are adopted by public and private organizations to encourage users to switch to sustainable transport for daily commutes. Originally, mobility management only focused on employees, but recently various universities worldwide have started to adopt policies specifically targeted at students. To improve their effectiveness, an innovative approach to TDM based on a GPS smartphone application is tested at University of RomaTre (Italy) on a sample of students. The strategy identifies the integration of all mobility management activities into one smartphone application called IPET (Individual Persuasive Eco-Travel Technology). The IPET enables activity-travel data collection and automatic processing, personalized information provision, use of persuasive technology and implementation of a gamification system. Two questionnaires were conducted before-and-after the measure, to assess its effects on psycho-attitudinal factors. The main objective of the investigation was to analyze individuals’ voluntary behavioral changes with regard to travel mode and everyday behavior following implementation of a personalized travel plan. Data analysis indicated a strong positive change in the level of awareness regarding CO2 emissions and calories burnt after the program implementation. Comparison of GPS data collected in the first and second weeks showed, for those who received a personalized travel plan, an 8.1% shift towards more sustainable transport modes.

Heterogeneity in departure time preferences, flexibility and schedule constraints

This study develops a latent class choice model of departure time preferences for morning commute trips by car. The model is empirically evaluated using a sample of car commuters (mainly) in the Greater Copenhagen region in Denmark. The model identifies three classes that differ in terms of their preferences for departure times, their schedule constraints and degree of flexibility, and their socio-demographics characteristics. Roughly 30% of the sample exhibits high flexibility and is quite willing to reschedule in response to ‘peak spreading’ travel demand management strategies; 50% of the sample is constrained in the afternoon and evening, and consequently, less responsive to these strategies; and 20% of the sample is constrained in the morning and afternoons, and least likely to reschedule. We demonstrate the value of our model framework for policy analysis over simpler choice model frameworks that do not explicitly account for the existence of population segments with distinct preferences for departure time behaviour. In particular, we demonstrate how forecasts from our model may differ substantially from corresponding forecasts from more conventional choice models.

Optimal design of promotion based demand management strategies in urban rail systems

Travel demand management (TDM) is used for managing congestion in urban areas. While TDM is well studied for car traffic, its application in transit is still emerging. Well-structured transit TDM approaches can help agencies better manage the available system capacity when the opportunity and investment to expand are limited. However, transit systems are complex and the design of a TDM scheme, deciding when, where, and how much discount or surcharge is implemented, is not trivial. The paper proposes a general framework for the optimal design of promotion based TDM strategies in urban rail systems. The framework consists of two major components: network performance and optimization. The network performance model updates the origin-destination (OD) demand based on the response to the promotion strategy, assigns it to the network, and estimates performance metrics. The optimization model allocates resources to maximize promotion performance in a cost effective way by better targeting users whose behavioral response to the promotion improves system performance. The optimal design of promotion strategies is facilitated by the availability of smart card (automated fare collection, AFC) data. The proposed approach is demonstrated with data from a busy urban rail system. The results illustrate the value of the method, compare the effectiveness of different strategies, and highlight the limits of the effectiveness of such strategies.

Modelling the determinants of car-sharing adoption intentions among young adults: the role of attitude, perceived benefits, travel expectations and socio-demographic factors

Major cities in developing countries are increasingly becoming motorized. Thus, effective solutions to address the negative impacts that come with rising car-ownership are needed as part of an overall travel demand management strategy. In developed and emerging economies, shared-mobility in the form of car-sharing is becoming popular as potentially low-cost and environmentally sustainable alternative to car-ownership. Yet, our understanding of car-sharing adoption and diffusion factors in developing countries is limited. In this study, we fill this gap by examining car-sharing adoption intentions among young adults aged between 18 and 35 years in Ghana, Sub-Saharan Africa. Using structural equation modelling, we model car-sharing adoption intentions based on a framework that integrates individuals’ perception of the benefits of car-sharing, attitudes towards the environment and technology, trust of stewardship in car-sharing, perception of innovativeness of car-sharing, travel expectations and socio-demographic factors. We found that pro-technology and pro-environmental attitudes correlate positively with perceived benefits of car-sharing. Perceived benefits of car-sharing, in turn, has the largest predictive effect on intentions to car-share. Other factors, including individuals’ previous experience using Uber on-demand taxi services, gender, education, driver’s licensure and expectation of comfortable and fast travel options, all predict car-sharing adoption intentions. While there exists an interest in both station-based and free-floating car-sharing services, more of the would-be users favour the latter than the former. Also, majority of the potential adopters (62%) would join a car-sharing service within the first 1 year of its introduction. An important finding is that dissatisfaction with existing public transit services underpins car-sharing intentions, implying that relying on car-sharing alone to meet travel needs, without a holistic strategy of providing quality and affordable public transit services, could lead to unsustainable outcomes.

Making use of elicited confidence ratings of the SP survey in a joint RP-SP model: a case study of commuting mode choices for TDM evaluations

ABSTRACTThe paper proposes a joint econometric model to investigate the effects of inertia; elicited confidence rating on SP choices and socio-economic variables on the choice model parameter estimates. The proposed model is estimated empirically by using a dataset collected in the Region of Peel, Ontario, which was designed to collect data for evaluating employer-based Travel Demand Management (TDM) strategy evaluation. Empirical models reveal that the accommodation of inertia effects and socio-economic attributes in the joint RP-SP model mainly improves the goodness-of-fit of the choice model. However, capturing endogeneity between SP choice and elicited confidence rating improves the efficiency of parameter estimates by increasing the statistical significance of the choice model parameters. The paper shows that there is the profound benefit of collecting confidence rating information in SP choice experiment and exploiting such information to enhance choice model parameter estimates.