Travel Demand Management Policies Research Articles

Jointly modelling activity start time, travel mode, companionship, and destination location choices

Activity-travel decisions, including time use (i.e. activity start time), travel choices (i.e. mode and companionship), and destination location choices are interdependent and complex in nature. They are often controlled by observed factors such as sociodemographic, vehicle ownership, built environment, land use, and unobserved factors like attitude, preference, or habits which typically are not captured in the survey data. To accommodate these complex interactions and capture trade-offs among the time use-travel-land use choices, this study proposes a joint discrete choice model by introducing unobserved factors that are common to them. The model results confirm the presence of complex interdependencies among these choice dimensions. In addition, this study highlights the contribution of observed and unobserved factors introduced in the model by calculating the total variance of utility differences. Lastly, this study provides behavioural insights on the activity-travel patterns, which can be further used to develop robust travel demand management policies.

Acceptability toward Policy Mix: Impact of Low-Carbon Travel Intention, Fairness, and Effectiveness

Policymakers are implementing a combination of various travel demand management (TDM) policies to effectively reduce carbon emissions. Widespread acceptance of these policy mixes by residents plays a pivotal role in ensuring their successful implementation. Drawing upon the value–belief–norm (VBN) theory and policy-specific beliefs (perceived freedom, perceived fairness, and perceived effectiveness), this study evaluates the impact of low-carbon travel psychology and policy characteristics on the acceptability of a policy mix formed by the combination of mandatory measures and guiding measures within TDM. The empirical analysis leverages survey data obtained from 839 participants and employs a structural equation model. The results indicate that low-carbon travel willingness, perceived fairness, and perceived effectiveness significantly influence acceptability. Consequently, fostering low-carbon travel willingness among residents is essential for enhancing overall policy acceptability. Moreover, policy mixes that encompass incentives for the development of new energy vehicles (NEVs) are generally considered the most acceptable among residents due to their non-restrictive impact on private car usage. Given this analysis, policymakers should consider designing policy mixes that balance acceptability and effectiveness, such as incorporating policies for the development of NEVs, to address traffic pollution.

A mode shift Bayesian network model for active travel demand management policies

Promotion of mode shift from private to public transportation is one of the difficult tasks in modern travel demand management (TDM) especially for travelers in auto-dependent cities. This study investigates the role and impact of two active travel demand policies, i.e. incentive program and info-mobility system, on encouraging mode shift to public transportation with a questionnaire survey. The incentive program provides monetary or other form of incentives to encourage travelers to alter their travel behavior spatially or temporally. The info-mobility system refers to the use of information technology to disseminate real time travel information to travelers. A total of 670 respondents are surveyed in the Klang Valley region, Malaysia. Bayesian Network Modeling (BNM) is adopted as the analysis tool to predict the traveler mode shift intention. The developed model, termed as Mode Shift Bayesian Network Model (MSBaysnet), shows that: (1) the presence of an incentive program could change travelers’ perceived convenience of using public transportation, especially if they stay in the vicinity of public transportation stations; (2) the info-mobility system should be considered as a crucial component of an integrated public transportation system; (3) Bayesian Network has an advantage in modeling complex interactions of multiple TDM strategies; (4) no standalone TDM strategy works in promoting mode shift intention and synergistic effect of multiple policies is required to encourage mode shift. The findings derived from this study can aid policy maker in formulating useful mode shift strategies to improve public transportation ridership.

Effectiveness of Travel Demand Management Policies in Promoting Rail Transit Use and Reducing Private Vehicle Emissions: A Stated Preference Study of Bangkok, Thailand

In this study, we focused on policies to promote reduction in the use of private vehicles that could be implemented in Bangkok during periods of severe PM2.5 levels, including a flat charge for use of private cars, private vehicle bans, and public transport fare subsidization. The objective was to investigate how these policies can be used to help convince private car users to shift their travel modes to rail transit, and, thus, reduce vehicle emissions that contribute to air pollution. We conducted a stated preference survey of 731 private car users in Bangkok, Thailand, where stated-choice scenarios were specified with respect to all possible combinations of the proposed policies that could lead to a reduction of PM2.5 concentration. A binomial logit model was used for the analysis. Our results suggest that the effectiveness of the travel demand management policies would vary across geographical areas of the city. Public transport fare subsidization would be effective in reducing car use among residents in the inner suburbs. A car ban would be an effective measure in outer suburbs, while the flat charge would be effective among populations in central Bangkok and the inner suburbs.

Are Telecommuters’ and Non-Telecommuters’ Daily Time-Use Behaviors Different? An Episode-Level Model for Non-Mandatory Activities

Telecommuters (individuals working from home), and non-telecommuters (individuals working at the workplace) might have significantly different daily time-use patterns. For example, telecommuters might be able to engage in more episodes of non-mandatory activities such as recreation and pick-up/drop-off of children, as many telecommuters have a relatively flexible work arrangement in relation to their work location and schedule. This study aims to explore the non-mandatory activity engagement and duration decisions of these two worker profiles at a disaggregated-episode level. To do so, a multiple discrete continuous extreme value with ordered preferences (MDCEV-OP) model is adopted, using the 2018 travel survey data from the Central Okanagan region of British Columbia, Canada. This model accounts for multiple occurrences of each activity type (i.e., episodes) along with their corresponding durations. In doing so, the model adopts a logical ordering ensuring that the jth episode is not predicted without the occurrence of the ( j–1)th episode of an activity type. The model results reveal that telecommuters are more likely to engage in higher episodes of non-mandatory activities, compared with non-telecommuters. For instance, female teleworkers are found to be more likely to participate in higher episodes of activities associated with household-related responsibilities such as shopping. In contrast, female non-telecommuters are associated with participating in more episodes of personal business-related activities. The findings of this study provide important behavioral insights into the activity-time-use patterns of telecommuters and non-telecommuters, which can be utilized to develop more effective and equitable travel demand management plans, policies, and models.

On a Flexible Car Use Restriction Policy: Theory and Experiment

Car use restrictions have been adopted in some mega cities that experience rapid car ownership increase and worsening traffic congestion. Although easy to implement and considered fair, most implementations of this travel demand management policy do not offer travelers the flexibility to choose the days that they cannot use their cars. In this paper, we study a flexible car use restriction policy under which a private car cannot be driven on a certain day of a week, but the day can be chosen by its owner. Under this flexible policy, individuals face a dilemma between driving in congestion and traveling without a car, each incurring a cost of its own. The resulting equilibrium solutions under these two competing choices were derived, and a series of laboratory experiments were carried out to validate the theoretical results. The experimental results are found to be in agreement with the theoretical results. Moreover, our analysis shows that the flexible car use restriction policy reduces the average travel cost with a lesser increase in average driving cost when compared with the traditional car use restriction policy. Funding: Z.-Y. Gao was supported by the National Natural Science Foundation of China [Grant 71621001]. W.-X. Wang was supported by the National Natural Science Foundation of China [Grant 71631002]. R. Jiang was supported by the National Natural Science Foundation of China [Grant 71931002]. X.-Y. Sun was supported by the National Natural Science Foundation of China [Grant 71961002]. B.-Y. Zhang was supported by the National Natural Science Foundation of China [Grants 71922004 and 72131003]. X. Han was supported by the National Natural Science Foundation of China [Grant 71801011] and by the China Postdoctoral Science Foundation [Grant 2018M631331]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.1200 .

Parking Charges: Ingeniously Effective and Publicly Accepted in Riyadh?

The background of this study is relevant to parking facilities at shopping malls in the Kingdom of Saudi Arabia (KSA), where all parking services are mostly free, with very few exceptions. Demand on parking places at shopping malls and recreational areas in Saudi cities are generally very high. This is partly because most of the entertaining, shopping, and recreational areas are indoors and are typically located at shopping malls with huge parking provision. With the vast increase in car ownership and use in the country over the past few decades, a consequent increase in demand on roads and parking facilities has been observed, which has no doubt resulted in further congestion. This study aims to investigate the willingness to pay to save time searching for a parking place at shopping malls in Riyadh city in the KSA. The methodology includes interviewing shoppers in shopping mall car parks and asking about their search time for a parking space and their willingness to pay to save search time, as well as to report on their socio-economic characteristics including age, gender, employment, education, car ownership, and income. The outcome of this study shows that on average, shoppers spend about 9 min searching for a parking place for their shopping and recreational trips. The results also show that, on average, shoppers reported that they go shopping, eating out, etc., about three times per week, and the average willingness to pay to save time searching for parking space is about 10 Saudi Riyal (about 2.7 USD) per visit. The modelling results show that there is a higher willingness to pay from the middle income and car-ownership categories of the population than from the highest income and car ownership groups. This might reflect the fact that the middle-income groups are much more dependent on themselves to drive their cars and search for a parking space every time, while those of higher incomes could possibly rely on their private drivers most of the times to drive them and park their cars. This paper contributes to the literature by providing a first understanding on how demand for parking facilities is affected by various socio-economic factors and the willingness to pay to save search time at these parking locations. The implications of the outcome of this research will be of use for decision makers and city authorities in planning travel demand management policies and programs that aim at reducing demand on private cars and achieving sustainability.

Modal shift behavior of car users to light rail transit, some evidence from the field

Growing population and car dependency in developing countries have led to congestion that adversely affects the environment, travel time, trip cost, accidents, and public transportation reliability. Through implementation of travel demand management (TDM) policies, governments and policymakers aim to reduce private vehicle dependency and encourage people to use public transportation. Light rail transit is an important part of an attractive public transport. Rail transportation systems, though offer many potential benefits, are a major financial challenge for governments because of the high capital and operation costs. Therefore, passenger behavior must be determined before a new system is introduced. Using a stated preference (SP) questionnaire, private car users' behavior in Tehran's universities has been investigated to determine the explanatory factors affecting the modal shift to urban light rail transit (LRT). A binary logit model estimation results showed that men are less likely than women to shift toward LRT because they rely more on their private cars. It was the provision of free on-street parking at destinations and the frequency of using private cars on a weekly basis that had the most negative effect on LRT modal shift, demonstrating the critical role that implementation of TDM policies could play. Moreover, reduction in travel time by LRT has the most positive impact on modal shift toward LRT among private car users. As an interesting finding, marginal effect values indicate that a 10% reduction in travel time (0.32) has a greater impact than the possibility of sitting 50% of the travel time (0.25) on the likelihood of modal shift to LRT. Furthermore, the probability of modal shift to LRT will be reduced by 0.12 units for each unit increase in car ownership. In addition, owning a driver's license was also negatively correlated with LRT modal shift and decreased the likelihood by 0.27. This research will facilitate the decision-making and planning for future transportation systems to increase LRT's utility for potential users.

Travel demand management policies: A case study of Singapore and transferability potentials for Hanoi

This research study investigates the travel demand management (TDM) strategies and solutions and associated policies through a case study of Singapore to gain knowledge and experiences, and to evaluate the potentials for policy transfer in TDM from Singapore to Hanoi, the Capital of Vietnam. The research follows a qualitative approach that critically analyzes policies from international perspectives for lesson-drawing. The ideas generated in the literature review are reinforced by the case study of Singapore that examines relevant issues to acquire insights into policy transfer initiatives in TDM. The research identifies considerable differences between Singapore and Hanoi with respect to the development objectives, social and political settings and the governance structure of the city in general, and in transport sector in particular. The results show that there are critical political, institutional, physical and social constraints on policy transfer from Singapore to Hanoi. Given the stated critical constraints, it could be premature to conduct transfer of TDM policies from Singapore to Hanoi for the time being, but the findings from the case study have significant policy implications to Hanoi, and lesson-drawing is highly valuable and attainable. An initial screening was conducted to evaluate the applicability of policies used in Singapore as a starting point to develop a comprehensive TDM framework for Hanoi. The findings of the case study in critical contextual constraints for policy transfer should be useful in developing a roadmap for the transfer of TDM policies to a growing city like Hanoi.

A game theoretic approach to study the impact of transportation policies on the competition between transit and private car in the urban context

This paper proposes a spatial gaming model to characterize the mode choice behavior of travelers on a corridor connecting a suburban neighborhood to the central business district. Previous game theoretic studies typically incorporate a discrete choice model into the gaming structure to observe travelers’ mode choice behavior. The novelty of the proposed model revolves around incorporating parking operator into the gaming structure. We also present a model extension accounting for the heterogeneity in travelers’ income. The parking operator and transit agency, as the two players of the game, seek to obtain a greater market share by controlling their prices. The nature of the transit agency as a (pseudo) public authority is modeled by letting the player maximize a weighted sum of profit and consumer surplus. The private parking operator, on the other hand, is a profit maximizer. With the out-of-pocket cost, generalized travel time, and disutilities other than time in the utility function of travelers, this gaming model enable us to evaluate the impact on auto-transit competition of a wide range of urban policies. A corridor in Isfahan, Iran is considered to demonstrate the applicability of the proposed model. Eight travel demand management policies are examined. We see that restrictive policies (e.g., cordon pricing) typically reduce the total demand while encouraging policies (e.g., improving transit access time by integrating transit with a bike-sharing system) increase it. The most effective policies are found to be hybrid policies such as increasing auto travel time coupled with lowering transit in-vehicle travel time.

Connected automated vehicle impacts in Southern California part-II: VMT, emissions, and equity

Connected and automated vehicle (CAV) technologies are likely to have significant impacts on people's travel behaviors and the performance of transportation systems. This study investigates the impacts of CAVs from various aspects, including vehicle miles traveled (VMT), emissions, and transportation equity in Southern California. A comprehensive model is developed by incorporating the supply-side improvement of CAVs, a modified activity-based demand model supported by survey data, and a multi-class highway assignment model. The simulation results showed that VMT and emissions would increase by 10%, and CAVs could worsen travel equity across income groups. To reduce the negative impacts caused by CAVs, we proposed and evaluated a series of travel demand management policies. The results indicated that all policies help to reduce the VMT and emission growth, while their performances in enhancing travel equity vary across metrics including accessibility, travel frequency, and travel distance.

Modelling commuting tours versus non-commuting tours for university students. A panel data analysis from different contexts

University students’ mobility represents a significant part of the mobility demand, since the right to mobility becomes yet more significant, as it directly translates into the right to education. At the same time, lifestyle evolution and changes has yield to a boost in the number of non-commuting tours, which are now recognized as a key component of any travel demand system. However, their analysis is often overlooked due to their randomness and difficult detectability.Motivated by this shortfall, the current study sought to explore the university students’ mobility by focusing on i) a comparison among commuting and non-commuting tour, ii) analysing non-commuting patterns and iii) identifying factors affecting the tour generation. A joint mixed logit model was specified and estimated using panel data collected in two Italian Universities (Cagliari and Rome).This study represents a pilot test conducted for the purpose of providing scientific justification for implementing Voluntary Travel Behaviour Change programmes and Travel Demand Management policies in Italian Universities. Our results indicate that the number of non-commuting tours, when compared with commuting tours, is not negligible (around 28% of tours are non-commuting tours) and we detected no-significant differences between Cagliari and Rome with respect to the tour characteristics. In Cagliari women, individuals who have a high number of household members, people living in areas characterized by high building densities and a small number of shops, and in Rome students living in small families and those who own a car, are more apt to travel for discretionary purposes.

Analyzing the impact of the built environment on commuting-related carbon dioxide emissions

It is critical to understand the elements that influence CO2 emissions from commuting to establish low-carbon transportation and land-use regulations. Research attempted to determine the mechanisms by which the built environment (BE) influences commuting-related CO2 emissions. Most research was conducted in developed nations and used traditional modeling to evaluate the relationship between BE and CO2 emissions primarily considering direct consequences related with BE. There is a research vacuum in predicting the total impacts of BE on CO2 emissions from commuting, taking into account the mediating effect of car ownership. This research examines total effects of the built environment (BE) on commuting-related CO2 emissions including both direct and indirect effects. We used 10,592 home-based work trips from Dhaka, a developing city, to create a structural equation model (SEM) for predicting this association. We included car ownership as a mediating variable and treated BE, car ownership, and CO2 emissions as endogenous variables. Both population density and land-use diversity are positively associated with private car ownership. The study shows the built environment plays a different role in explaining CO2 emissions from commuting in developed and developing countries. Population density has a direct positive impact on CO2 emissions, as evidenced by previous research in a developing metropolis. Because of its mediating effect on car ownership, land use diversity has a considerable positive indirect impact on emissions but a negligible overall effect, making it ineffective on its own and necessitating the implementation of complementing Travel Demand Management (TDM) policies. Our modeling results are comparable with those from both developing and developed countries in terms of public transportation accessibility, job accessibility, and road network design. The results might be used to produce policy guidelines to reduce car ownership and CO2 emissions, which would help developing countries in South Asia meet their sustainable development goals.

The impact of migration background, ethnicity and social network dispersion on air and car travel in the UK

While there is a saturation of car mileage per head in developed countries, international air travel has grown rapidly until 2020, making it crucial to better understand its drivers. An often-overlooked factor here is migration. Previous research suggests that individuals with migration background and/or from ethnic minorities tend to have more environmentally sustainable everyday travel patterns, due to lower car use. Yet there is evidence suggesting that their air travel-related emissions are higher than average, possibly due to visits to the home country and the trips required to maintain spatially distant social ties. However, migration background and social network attributes are typically not included in quantitative studies of air travel. In this paper, we analyse data from the 2011–2013 UKHLS survey, which provides information on annual car mileage and frequency of air travel for private purposes, allowing us to derive rough estimates of greenhouse gas emissions. We estimate regression models for these outcomes, including migration background, ethnicity, and social network predictors. We find that: i) first-generation migration background is associated with lower car mileage, but higher levels of air travel; ii) this effect is less pronounced or absent for less recently-arrived migrants and second- and third-generation migrants; iii) spatially dispersed social networks are positively associated with both air and car travel; iv) the association between migration background and air travel is partly accounted for by social network dispersion. We conclude by discussing implications for future research and air travel demand management policies.

Reducing the carbon footprint in college mobility: The car commuters' perspective in an Italian case study

Despite the negative environmental impact of private cars, especially when driven alone, thousands of college commuters around the World are still car dependent. In order to promote eco-friendly commuting and reduce the GHGs caused by cars, different Travel Demand Management (TDM) policies are in place at many colleges, but they do not usually consider the car commuters' point of view when identifying proper measures. In this paper, by using survey data from an Italian medium-size public university, we study what TDM policies are perceived by car users as the most effective in reducing the number of solo-drivers and consequently their carbon footprint. By estimating a multinomial logit model, we found that restrictive parking policies are statistically supported by female car commuters, students and employees belonging to science departments, and those who know that driving alone could impede alternative modes. Indeed, transit-oriented and multimodal options (including bikes) are chosen by faculty/staff car users, by users willing to leave their cars at home when the weather is good, and in general by people living in towns without railway stations. Instead, probably due to inadequate rail services, car users living in areas far from the college are weakly inclined to use transit-plus-bikes modes. Beyond improving TDM policies, these results can help mobility managers design localized tools to reduce the environmental impact of college commuting.

Trends in air travel inequality in the UK: From the few to the many?

Aviation is responsible for at least 3.5% of global warming, and demand is predicted to rise rapidly over the next few decades. To reverse this trend, air travel demand will need to be managed. An important question is: ‘who would be affected by air travel demand reduction policies’? The answer to that question largely depends on who is participating in air travel, and how unequally it is distributed. Existing analysis suggests that participation in air travel in the UK is highly unequal and driven by richer, highly educated and urban households. However, so far little is known about how these patterns of inequality have changed over time – has air travel participation increased among low income households, e.g. due to the rise of low-cost carriers and ‘normalisation’ of air travel as a social practice? Would these groups therefore now be more affected by flight taxes or frequent flyer levies? To address these questions, this paper examines trends in air travel inequality between 2001 and 2018 in the UK based on two representative surveys, providing the first micro-level analysis of air travel inequality over time for this country. We find that while disadvantaged groups have contributed to the expansion of air travel over the past two decades, they remain far less likely to be affected by air travel demand management policies because air travel inequality is still at a very high level. These findings challenge common discourses that present air travel as a widespread norm, and demand management policies as socially unfair.

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 General System Model of Vehicle TDM Effect on Urban Road Traffic

Rapid growing congestion in urban areas lead to wide adoption of travel demand management (TDM) policies. However, the effect evaluation of TDMs encounters great difficulty under existing traffic simulation models which in common require a complex road link system to be built and accurate attributes to be assigned to each link. To avoid the fallible and difficult establishing and calibrating process of existing methodologies, a novel way to analyze TDM is proposed in this paper. With travel demand being described as free-flow travel time of a trip and several macroscopic concepts defined, a theoretical methodology is proposed to reveal the interactions between supply and demand. Then the simulation of TDM is transformed into a classic state space analysis problem. Based on this methodology, the general system model of TDM effect on road traffic operation is set up. The proposed model is tested with three TDMs, including driving restriction, staggered shifts and change of trip length distribution. The results prove that the proposed model can provide fast analysis for TDM policies and has a broad application prospect in macro traffic simulation.

Estimation of highway capacity under environmental constraints vs. conventional traffic flow criteria: A case study of Tehran

Abstract In this paper, the concept of environmental capacity is developed to identify a convenient maximum traffic volume which will not reduce the life quality of residents. The presented method investigates the idea of traffic capacity under environmental constraints by calculating the maximum number of vehicles allowed on roads based on acceptable levels of air and noise pollutants. In this study, the permissible noise pollution level and permissible levels of CO and NOx pollution are considered for determining environmental capacity. Results show the significant difference between environmental capacity and functional traffic capacity, introduced by the highway capacity manual (HCM) as a conventional method for estimating functional capacity. Thus, maximum allowed pollution is considered a constraint on a vehicle flow rate, which shows the proper traffic flow for selected streets in Tehran, Iran's capital. The paper concludes that traffic capacity under noise and air pollution constraints is much less (approximately one-fourth and one-eighth for noise and air pollution respectively) than the current highway capacity estimated using HCM guidelines. Therefore, to save the cities like Tehran from noise and air pollution, traffic flows should be limited to the level of environmental capacity by implementing some travel demand management (TDM) policies like road pricing.

Exploring tertiary students' travel mode choices in Auckland: Insights and policy implications

It is crucial that policymakers and public transport operators comprehend tertiary students' travel mode choices and understand the factors that inform these choices in order to manage travel demands effectively and to optimise the use of public transport and improve its quality of service, particularly during the peak hours. This study aims to examine tertiary students' travel mode choices in Auckland, which is recognised as one of the most car-dependent cities in the world. The city is struggling with traffic congestion, particularly around its Central Business District (CBD) during peak hours. Auckland is New Zealand's largest city, with a population of 1.4 million residents. The main campuses of two major public universities, The University of Auckland (UoA) and Auckland University of Technology (AUT), are located in the CBD, attracting a large number of daily trips by both staff and students, particularly during the teaching semesters. However, there is a limited understanding of tertiary students' travel mode choices, particularly the factors that inform these choices when they travel to the universities' city campuses. A mixed methodology approach was used to comprehend the travel mode choices of students attending The University of Auckland and to identify the key factors that are drivers of these choices. The data collection included a questionnaire-based survey, which received 249 responses, and 10 semi-structured interviews with students. Thematic analysis was utilised to codify and then analyse the interviews. Despite the significant car dependency in Auckland, the survey demonstrated that most respondents utilised public transport and active modes when commuting to the university's city campus. Seven factors were identified that inform tertiary students' travel mode choices: cost, parking availability and cost, access to a car, travel time, physical environment, reliability, and attitudinal variables. The interviewees mostly argued that travel cost and lack of or limited access to a private car were the primary drivers of their travel mode choices. The study suggests that different stakeholders, such as Auckland Transport (AT) and The University of Auckland, should work collaboratively to provide an inclusive travel demand management policy. The university could rearrange classes for off-peak hours, and AT could offer tertiary students further discounts during these hours. These actions would result in the optimisation of public transport efficiency, improvement of the quality of the public transport service, and mitigation of traffic congestion around Auckland's Central Business District (CBD).