Travel Demand Management Measures Research Articles

Examining Congestion Pricing Scheme Effectiveness Using the Travel Time Congestion Index

This study investigated the effectiveness of congestion pricing (CP) using the travel time congestion index (TTCI), a congestion assessment tool, in Hyderabad, India. Initially, a set of hypothetical mode choice scenarios under the CP scheme were designed to collect car users’ perceptions based on a stated preference (SP) experiment. Based on the SP survey data, discrete travel behavior models were developed to estimate the probable modal trade-off among cars, two-wheelers, and public buses under the generated CP scenarios. Using the existing traffic, geometric, and land-use data from the most congested corridors of the study city, TTCI values were estimated for the base and future conditions, followed by commuter volume estimation for the different conditions using vehicle occupancy factors. Further, the commuter volume derived from modal trade-off under CP scenarios was converted into traffic volume for the identified congested corridors. Finally, the TTCI values were re-estimated using the final traffic volume and compared across worst, best-worst (intermediate), and best case CP scenarios for the base and future years. An annual average growth of 5% in traffic volume was considered. The results show a significant improvement in TTCI values across all identified corridors under CP implementation, indicating its effectiveness toward congestion alleviation. Such demonstration of CP effectiveness could play a major role in making CP a successful travel demand management measure for cities burdened with congestion.

Travel demand management strategies to mitigate climate change

As urbanisation intensifies worldwide, the associated increase in travel demand poses significant challenges to mitigate climate change. This study examines the implementation of travel demand management (TDM) strategies within the context of urban development, focusing on the case study of Seoul's green transport zone. Through a comprehensive analysis of transportation, environmental, health, spatial reorganisation and economic aspects, this research study demonstrates the effectiveness of TDM in reducing greenhouse gas emissions and promoting sustainable urban growth. The findings highlight the importance of integrating TDM measures into urban planning efforts to achieve resilient and environmental-friendly transportation systems.

A comparative analysis of University Sustainable Travel Plans – Experience from Australia

Travel Demand Management (TDM) initiatives are widely applied by transport planners to establish and enable appropriate use of critical transport infrastructure. Less attention has been given to the specific case of TDM in an education precinct (university) context. Travel Plans have been promoted as a means for an organisation to encourage sustainable travel choices by their employees, visitors and customers. This paper offers an empirical contribution to the literature through a comparative qualitative evaluation of selected University Sustainable Travel Plans (USTPs) in Australia to identify the most important questions that a USTP should address explicitly. The evaluation comprised identification of a set of evaluation questions, completion of a template for each USTP considered and application of a simple scoring exercise. We also identify TDM measures that have been introduced as part of a USTP in response to the typical travel patterns exhibited in university settings. A contribution of this paper is to create a means of comparison of USTPs and to establish the components of a comprehensive travel plan.

Travel Demand Management in an Auto Dominated City: Can Travel Behaviour Be Nudged in the Kingdom of Saudi Arabia?

Car ownership and use in the Kingdom of Saudi Arabia (KSA) are very high due to the high income, the low fuel prices and the near absence of public transportation in the Kingdom. Currently, the Kingdom is going through a massive transformation and entering a new era of national reforms. One of the main aims of the national reforms is to reduce car dependency and enhance more sustainable options of travel such as public transportation in the KSA. In order to achieve such an aim, there are two hurdles: Firstly, there must be a provision of decent public transportation options, and secondly, there is a need to influence travel behaviour and encourage the shift from private cars to public transportation. For example, in Riyadh city, an impressive metro system is being constructed and will start operation in 2023. To influence travel behaviour, travel demand management measures (TDM), in particular pricing measures, can be adopted and implemented, in order to help and support achieving the target. The main aim of this paper, therefore, is to assess the attitudes of Saudi nationals towards—and willingness to accept—pricing measures and their possible impacts on their travel behaviour in the city of Riyadh. The methodology includes collecting data using an online survey on travel behaviour and attitudes in Riyadh and calibrating multinomial logit modal choice models. The participants in the survey were asked to report their support of the pricing measures for the objective of reducing congestion in the city, improving road safety or reducing travel time. The results show the highest support towards pricing measures for improving road safety, reducing travel times and, lastly, reducing congestion in the city.

A discrete-event public transportation simulation model to evaluate travel demand management impacts on waiting times and crowding conditions

Several approaches have been proposed and adopted by researchers and decision-makers to improve and deal with public transport operation issues, especially travel demand management (TDM) measures. Disruptions like lockdowns provoked by weather conditions, political riots, special events, natural disaster issues, or the recent COVID-19 pandemic create a need for tools to manage public transport demand and supply o keep users circulating in an efficient, convenient and safe manner. Our work develops a simulation tool of the operations of a public transport system using smart card, GTFS and census data to evaluate the impacts of different intervention scenarios using the pandemic context as a case study. Using a pre-pandemic baseline scenario, we study the impact of several travel demand and public transport supply measures, focusing the analysis on waiting times and crowding conditions inside vehicles and platforms. As a result, we generate easy-to-analyze visual outputs that facilitate prioritizing actions at the metropolitan and district level, identifying where and when waiting times and crowding conditions would exceed certain thresholds.

Breaking commuter mode-use habits: An exploration of deliberative decision-making windows and their implications for travel demand management

It is widely accepted that travel behaviour can be habitual. It is also widely accepted that voluntary Travel Demand Management (TDM) seldom records city-wide mode-switching impacts. If travel choices are habitual, this is unsurprising, as much of the population are not making deliberative choices, and new information is not considered. Shifts to deliberative choice-making are posited to occur when a ‘stressor’ (e.g., changing job location) is experienced, which renders the travel habit no longer satisfactory. A common conceptualisation of these moments is that the stressor triggers deliberation, manifested in information-seeking and experimentation, to find a new satisfactory behaviour. If found and implemented, a new habit may be formed. This conceptualisation has implications for improving TDM impacts. It is tempting to assume that voluntary TDM measures should target those who have just experienced such stressors, as they are likely to be most receptive to deliberative change. This paper reports the findings of a retrospective survey of a purposive non-probability sample of (n = 250) Cape Town commuters who had experienced a habit-breaking stressor. A recall aid in the form of an ‘event history calendar’ was used to help create multiple memory recollection pathways of past commuting behaviour, and a ‘deliberation calendar’ was used to guide respondents in reporting the process of habit breaking. The study found that the trigger for deliberation and information-seeking was not the manifestation of a new residence or job location (together accounting for the majority of observed stressors), but around two months earlier when the decision-maker consolidated a plan of action. This finding contributes to a growing literature on the temporal dimensions of behaviour dynamics, and has implications for how TDM should be targeted. Targeting new homeowners or employees misses the ‘window of opportunity’ to influence deliberative decisions and new habits. These decision-makers need to be targeted sooner, while still house- or job-seekers.

Revisit employer-based travel demand management: A longitudinal analysis

Employer-based travel demand management (TDM) programs have been implemented worldwide for decades, but rarely are longitudinal analyses implemented. This study utilizes a longitudinal dataset to assess the effectiveness of TDM measures on vehicle trip rates (VTRs) over time. The results suggest: (1) VTR grows over time, and TDM measures may decelerate the growth but cannot reverse the trend; (2) for organizational tools, collective bargaining is negatively associated with VTR; (3) distributing transit passes is negatively associated with VTR; (4) ride match is positively correlated with VTR; (5) guaranteed ride home measures, including emergency rides and employer-provided vehicles, are positively associated with VTR; (6) subsidies promoting multimodal transportation (transit, walking, biking) incentivize vehicle trip reduction; the more subsidies, the lower VTR, and such effects are reinforced over time; (7) in contrast, subsidies promoting carsharing are associated with more vehicle trips; (8) larger companies and areas with higher job densities have a lower VTR. To inform practice, when rewarding employees, distributing transit passes is a preferred strategy. Collective bargaining builds agreements between employers and employees and helps promote trip reduction. Carpooling-related and guaranteed ride-home measures should be applied with caution.

Estimating mode choice of motorized two-wheeler commuters under the influence of combined travel demand management measures: An ICLV modeling approach

Several studies have reported the need for combining multiple Travel Demand Management (TDM) measures for minimizing the use of private vehicles. However, a detailed empirical analysis is needed to substantiate the theory and understand the factors influencing the resultant mode choice behavior. This paper presents a stated choice experiment study to examine the mode choice behavior under the influence of a combined TDM package of Congestion Pricing (CP) and Public Bike Sharing (PBS). The case study area is the old city of Ahmedabad, which is in the western state of Gujarat, India.A stated choice experiment was conducted, wherein a total of 1,719 data points randomly collected from 573 motorized two-wheeler commuters were used to simultaneously estimate an Integrated Choice and Latent Variable (ICLV) model for work trips in the study area. In addition to the observable variables associated with an individual's socioeconomic and trip characteristics, the model also includes the psychological theory of Value-Attitude-Behavior (VAB) in the choice modeling framework. The results show that there exists a statistically significant influence of values of benevolence and stimulation as well as attitudes towards the flexibility of mode, environment, and health in explaining individual preferences. Furthermore, the modeling results also suggest that the combined TDM measures are likely to significantly influence mode choice decisions as opposed to standalone TDM measures.

Clustered randomized controlled trial protocol of a Mobility-as-a-Service app for College campuses

This paper presents a protocol for a clustered, randomized controlled trial (RCT) in transportation planning. RCTs are still unusual in transportation planning, and this is the first RCT transportation protocol we are aware of seeking publication in a peer-reviewed journal. The trial tests a mobility-as-a-service app aimed at shifting mode choice to campus among an intervention student population while also tracking in parallel the mode choices of the control group. The concept behind the intervention is to leverage information and marketing to shift students' intentions relative to their travel to campus, following Azjen’s Theory of Planned Behavior. The app, which is custom developed under the guidance of the research team, facilitates students' trips to campus by transit, biking, walking, and ride-hailing. An intended secondary outcome of the intervention is improved academic outcomes among students in the intervention group. The research team developed the protocol through a literature review of campus-based travel demand management measures, then by vetting a draft protocol with an expert panel, and then through a series of survey pilots. The advent of COVID- 19 in the middle of the research project demonstrated the challenges of planning a real-world RCT where researchers have limited ability to control exogenous factors.

Evaluating Parking Characteristics of Park-and-Ride Facility at Gombak LRT Terminal Station

Malaysia has been experiencing a rapid increase in the number of private vehicles each year. This has increased in the use of private vehicles for various trip purposes especially work trips. Park-and-ride (PnR) is one of the travel demand management measures that would help to reduce traffic congestion and sustain urban mobility. The success of a park-and-ride facility, however, depends on many factors besides parking characteristics at the public transit stations. This paper evaluates the parking characteristics at one of the rail-based PnR stations of an LRT line. The parking characteristics at the selected rail station were evaluated by applying both a parking utilization survey and a pre-designed questionnaire survey. The parking utilization survey was administered by targeting all the parking spaces at each level of the parking building and a questionnaire survey by targeting a pre-determined sample of parking users. A total of 295 questionnaires were proportionately distributed to the parking users at each level of the PnR facility. The analysis reveals maximum utilization of the parking facility especially during morning and late afternoon hours representing a high proportion of work trip commuters. This finding was well supported when compared with the results from the questionnaire data analysis. There were no clear differences in the parking characteristics between the findings of this study and that of other studies. However, the parking behavior and the pattern of the parkers differ conspicuously from that of other studies. A few recommendations were drawn to overcome the unavailability of parking spaces.

Concept of Transit Oriented Development (TOD) and Implementation: Case Study of Pune

India is one of the developing nation and fastest growing economy in the world. India is facing rapid rapid population growth and it rank second in case of population. there is urbanisation going on so people are traveling to city for better life style, result in in stress on basic amenities, life style , employment ,housing and some other basic needs .Transit Oriented Development (TOD) is gaining popularity as a tool to achieve sustainable development in india . Transit oriented Development presents unique opportunities for indian city indian cities cities to meet challenges of the urbanization, inequity, quality of urban realm and climate change. Transit Oriented Development ( TOD ) include mix land use , transportation , street design, employment, green space etc. Transit Oriented Development being scientific and integrated development process between transport planinng and land use can be effective tool for attaining sustainable urbanisation . The objectives of this study are to assess TOD plans and proposals in select Indian cities to reveal their expected benefits (Pune). The TOD regulations in cities are being assessed in terms of transit benefits, land use mix, travel demand management measures and the provision of affordable housing . Based on the case of Pune, a planning framework would be developed to arrive at TOD strategies and measures for other Indian cities . This paper studies the concept of TOD and its advantage, challenges and case study.

A methodological framework for a priori selection of travel demand management package using fuzzy MCDM methods

A robust and scientific selection of appropriate Travel Demand Management (TDM) measures is likely to ensure that the purpose of their implementation is met. The existing methods of TDM measure selection do not often adopt a multi-dimensional approach in their decision-making process. Besides, there is minimal scholarly work on the selection of a ‘push plus pull’ integrated TDM package, even though it has been emphasized in the published literature. These gaps are addressed in our study, which adopts a novel approach of a Multi-Criteria Decision Making (MCDM) methodology. A combination of ‘Decision Making Trial and Evaluation Laboratory’ (DEMATEL) and ‘Analytical Network Process‘(ANP), along with the VIKOR, which is a ranking method used to select a suitable TDM package, a priori. The demonstration of the proposed methodology is presented for the case study area of Ahmedabad’s old city, which is located in the western state of Gujarat in India. A total of six TDM packages were initially shortlisted as probable contenders for the study area, each package consisting of one coercive (push) and one attracting (pull) measure. The MCDM tool ‘VIKOR’ was used to derive the ranking of these packages based on their performance in seven different criteria. The DEMATEL based ANP method was used to determine the weights of these criteria by comparing the influence of criteria over each other. The uncertainty and subjectivity involved in the entire process was taken into account by employing fuzzy logic into our proposed methodology. The research contribution of the study is a methodological framework that can assist policymakers in the a priori selection of a TDM package. The outputs from the study are twofold—(a) the Influence Network Relationship Map (INRM) and (b) the ranking of the TDM packages for the case study area. The INRM explains the relations between dimensions as well as between criteria within each dimension. Additionally, it classifies the dimension and criteria into cause and effect groups, which can help policymakers in developing a better understanding of the TDM implementation process. In our case study, the TDM package of congestion pricing and public bike sharing was the top ranked package. The most influential dimension was political feasibility, followed by private participation and public acceptability. In addition, effectiveness, investment attractiveness, and system resistance were the most influencing criteria within their respective dimensions.

Effectiveness investigation of travel demand management measures in Beijing: Existing measures and a potential measure–tradable driving credit

Facing serious challenges of traffic congestion and air pollution, Beijing has implemented a series of traffic polices. In this paper, we first investigate the effectiveness of existing traffic measures in Beijing and analyse the underlying factors from the perspective of drivers, which provide insights for the traffic regulations in other cities worldwide. While the car ownership restriction has effectively limited the total number of vehicle in Beijing, the effect of the car use restriction only lasts for a short period. A survey on drivers’ opinions and attitudes towards the traffic situation and a potential measure, i.e., tradable driving credit (TDC), is conducted. Although traffic data shows that congestion improved and traffic was slightly congested in recent years in Beijing, most respondents believe congestion is still serious in Beijing. However, they think the impact of traffic congestion on their personal car use is relatively low, which could partly explain why so many travellers still depend on driving given the relative convenient transit facilities in Beijing. In addition, a large proportion of respondents treat personal cars as a representation of social status and think driving bring them pleasures, which indicates that it may be hard to further decrease the share of car trips in Beijing. According to their attitudes and social-economical characters, respondents’ willingness on their changes of travel behaviours under TDC is investigated. The results show that individuals’ social-economic characters such as income level, education and family size and the symbolic meaning of personal cars have significant influence on respondents’ willingness to switch to other transport modes.

Impact assessment of short-term management measures on travel demand

Travel Demand Management (TDM) can be considered as the most viable option to manage the increasing traffic demand by controlling excessive usage of personalized vehicles. TDM provides expanded options to manage existing travel demand by redistributing the demand rather than increasing the supply. To analyze the impact of TDM measures, the existing travel demand of the area should be identified. In order to get quantitative information on the travel demand and the performance of different alternatives or choices of the available transportation system, travel demand model has to be developed. This concept is more useful in developing countries like India, which have limited resources and increasing demands. Transport related issues such as congestion, low service levels and lack of efficient public transportation compels commuters to shift their travel modes to private transport, resulting in unbalanced modal splits. The present study explores the potential to implement travel demand management measures at Kazhakoottam, an IT business hub cum residential area of Thiruvananthapuram city, a medium sized city in India. Travel demand growth at Kazhakoottam is a matter of concern because the traffic is highly concentrated in this area and facility expansion costs are pretty high. A sequential four-stage travel demand model was developed based on a total of 1416 individual household questionnaire responses using the macro simulation software CUBE. Trip generation models were developed using linear regression and mode split was modelled as multinomial logit model in SPSS. The base year traffic flows were estimated and validated with field data. The developed model was then used for improving the road network conditions by suggesting short-term TDM measures. Three TDM scenarios viz; integrating public transit system with feeder mode, carpooling and reducing the distance of bus stops from zone centroids were analysed. The results indicated an increase in public transit ridership and considerable modal shift from private to public/shared transit.

Machine learning applications in activity-travel behaviour research: a review

ABSTRACTThis paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.

Simulation-based joint optimization framework for congestion mitigation in multimodal urban network: a macroscopic approach

Travel demand management (TDM) is an important measure that will aid in the realization of efficient and sustainable transportation systems. However, in cities where the most serious traffic congestion occurs, implementation of a single TDM measure might not be enough to reduce congestion, because the congestion mechanism in this case is highly complex and involves different transportation modes interacting with each other. Implementation of multiple TDM measures has rarely been discussed in the literature. Therefore, in this study, we propose a simulation-based joint optimization framework composed of dedicated bus lanes and vehicular congestion pricing. The objective of the optimization process is to minimize the congestion cost based on an advanced macroscopic flow theory called the multimodal macroscopic fundamental diagram (mMFD), which can capture the macroscopic traffic dynamics of multimodal transportation systems. In the proposed framework, we develop mMFD-based congestion pricing scheme and incorporate traveler’s behavioral model (i.e. joint departure time and mode choices) with the microscopic traffic simulator. We consider the Tokyo central area as a case study. The simulation results indicate that space allocation of 4.7% for the dedicated bus lanes would be optimal for Tokyo’s network, while the optimal congestion pricing scheme indicates that charges of 900 JPY between 7:30 and 8:00 AM and 300 JPY between 8.00 and 8:30 AM should be levied.

Modelling of travel behaviour of students using artificial intelligence

Travel demand models are required by transportation planners to predict the travel behaviour of people with different socio-economic characteristics. Travel behaviour of students act as an essential component of travel demand modelling. This behaviour is reflected in the educational activity travel pattern, the timing, sequence and mode of travel of students. Roads in the vicinity of schools are adversely affected during the school opening and closing hours. It enhances the traffic congestion, emission and safety problems around schools. It is necessary to improve the safety of school going children by understanding the present travel behaviour and to develop efficient sustainable traffic management measures to reduce congestion in the vicinity of schools. It is possible only if the travel behaviour of educational activities are studied. This travel behaviour is complex in nature and lot of uncertainty exists. Selection of modelling technique is very important for modelling the complex travel behaviour of students. This leads to the importance of application of artificial intelligence (AI) techniques in this area. AI techniques are highly developed in twenty first century due to the advancements in computer, big data and theoretical understanding. It is proved in the literature that these techniques are suitable for modelling the human behaviour. However, it has not been used in behaviourally oriented activity based modelling. This study is aimed to develop a model system to predict the daily travel behaviour of students using artificial intelligence technique, ANN. These ANN models were then compared with the conventional econometric models developed. It was observed that artificial intelligence models provide better results than econometric models in predicting the activity-travel behaviour of students. These models were further applied to study the variation in activity-travel behaviour, if short term travel-demand management measures like promoting walking for educational activities are implemented. Thus the study established that artificial intelligence can replace the conventional econometric methods for modelling the activity-travel behaviour of students. It can also be used for analysing the impact of short term travel demand management measures.

The role of businesses in facilitating voluntary travel behaviour change - Insights from the London 2012 Olympic Games

The organisers of the London 2012 Olympics faced transport congestion challenges whose resolution required travel behaviour change across an entire transport network. This study evaluates the role of Transport for London's (TfL) £30 m business-focussed Travel Demand Management (TDM) programme in achieving significant traveller behaviour change across its entire transport network. The paper argues that soft TDM measures, together with a targeted ‘hard-edge’ message, alongside travel alternatives, can achieve significant voluntary travel behaviour change (VTBC) across a whole transport network, despite such behaviour normally being habitual.Incorporating a longitudinal study comprised of pre-and post-Games qualitative interviews, office attendance data and analysis of TfL's travel survey with Ajzen's (1991) Theory of Planned Behaviour, this research highlights the importance of reliable information, freedom of choice, and faith in the effectiveness of the measures proposed, in eliciting acceptance of the TDM policy. The results also demonstrate the importance of a relevant, hard-edge message in the form of a ‘big scare’—which operates as a ‘catalyst-for-change’ (CfC), breaking habit by increasing cognitive engagement and acceptance—as well as the importance of the role of businesses in delivering significant levels of voluntary travel behavioural change. The findings contribute to the evaluation of methods for achieving actual VTBC, which could be instrumental in enacting the large-scale travel behaviour changes required to meet international environmental objective of reducing carbon resource consumption by both businesses and individuals.

Future mobility and land use scenarios: impact assessment with an urban case study

In recent years the interest in urban mobility has grown considerably, not only due to the local increase in negative externalities generated by transport, but also because recent technological innovations are offering effective solutions especially in urban context. In particular, the introduction of Connected and Automated Vehicles (CAVs) could radically change the mobility scenario allowing, on the one hand, a widespread diffusion of shared vehicles that could feed the stations of the mass rapid transit network, improving the attractiveness of Public Transportation (PT), and on the other, the implementation of Travel Demand Management (TDM) measures on large areas of the most densely urbanized (and congested) territory of a city, without reducing accessibility and creating social exclusion. The present study aims at evaluating, through a system of Land-Use Transportation Interaction (LUTI) models, the impacts on transport demand and on population and activities location, of transportation policies oriented to both enhancing PT and restricting the individual use of the car. The case study analyzed is represented by the urban area of Rome. Several scenarios have been simulated and compared by means of sustainability indicators. Preliminary results show that the improvement of PT services, combined with the introduction of car use restriction and car free areas, do induce not only a significant modal shift towards more sustainable transportation modes, but also a limitation of urban sprawl.

Commuting Efficiency Gains: Assessing Different Transport Policies with New Indicators

This paper outlines new indicators for evaluating the probable impacts of introducing different land use/transport policies on the commuting efficiency of a city. It uses Beijing as a case study to describe how smartcard data can be used to derive a large number (n = 216,884, 9% of the population) of bus commuters’ workplace and residential locations. Using existing excess commuting indicators and new commuting efficiency gain indicators established to assess policy options, it exemplifies how to assess impacts of different policies on bus commuting efficiency gains. The case study indicates policies that directly target bus commuters (such as BRT) bring greater commuting efficiency gains when compared to other policies such as car usage restrictions and stringent travel demand management measures in the downtown area.