Private Car Trips Research Articles

Shared mobility - Novel insights on mode substitution patterns, trip and user characteristics

With a growing number of shared mobility options, choosing not to own a car may become a new norm in the future. Shared mobility offers users a flexible range of low or zero-emission alternatives, potentially reducing the use of private cars, as well as noise and air pollution. However, to date, there is little evidence of shared mobility acting as an alternative to private car use. Indeed, recent research suggests that shared vehicles not only substitute trips by private car, but also trips by active travel and public transport, thus not resulting in emission savings. Therefore, as part of the eHUBS project focusing on shared electric mobility hubs, the mode substitution patterns of 602 self-reported shared mobility users across Europe were investigated. More specifically, respondents of an online survey were asked to recall their last trip using shared mobility and to report on both their chosen and substituted travel mode in addition to common trip characteristics including trip distance and frequency. Chosen shared modes considered were both conventional or electric alternatives including cars, bicycles, cargobikes, and scooters. Overall, the results suggested that shared vehicles were about as or more likely to substitute public transport, cycling and walking, as private car trips, thus providing a mixed picture with respect to their potential contribution to reducing private car use and associated carbon emissions. Local authorities and policy makers are urged to actively continue to improve active travel facilities and to be proactive in better integration of shared mobility and public transport services.

Investigating the role of micromobility for first- and last-mile connections to public transport

The integration of private and shared micromobility with existing public transport services is often presented as a valuable step to improve urban mobility systems. In this study, the combination of micromobility and active modes with public transport was examined through a survey that included a stated preference experiment. Normal error component mixed logit models were estimated to determine how commuters in County Dublin, Ireland, travel on the first- and last-mile of public transport trips. The survey results shed light on the influence of user characteristics and mobility patterns on mode choice for multimodal public transport trips including private and shared micromobility, allowing to analyse trade-offs between the different micromobility modes and active modes. The mixed logit model analysis shows that young (<35 years old) and male respondents present a strong preference for micromobility, while older and female respondents prefer walking. Overall, walking is preferred by most respondents both for the first- and the last-mile, even if the other options provide significant travel time reductions. The importance of secure parking availability at stations is also reflected in the results and shows that a seamless transition between the modes and an adequate provision of infrastructure are crucial to facilitate micromobility adoption. Age, gender, previous experience with micromobility, and interest in micromobility are found to be significant factors that influence mode choice. These results confirm a growing interest for micromobility in younger generations, while also showing that regulations and adequate infrastructure are needed to promote equitable access to these modes, with a particular focus on attracting users from more carbon-intensive modes such as private car and ride-hailing trips.

How do the affective and symbolic factors of private car driving influence car users’ travel behavior in a car restriction policy scenario?

The number of private car trips a person makes is usually linked to external factors such as economic incentives or disincentives, legislation, infrastructure, and the quality of transport systems, among others. The impact of these options are typically analyzed to evaluate urban transport policies and projects. However, internal factors related to an individuals' attitudes and perceptions also play an essential role in the amount of private car driving they do and on urban mobility as well. Therefore, insight into private car driving's affective and symbolic aspects is important in order to formulate appropriate transport policies and strategies to reduce car-use dependency and encourage private car drivers to use public transport. Congestion charging schemes make drivers pay the full social cost of driving, they achieve congestion reduction, pollution reduction, they make cities more attractive for pedestrians and cyclists, and help improve quality of life. Congestion charging schemes are often difficult to implement and expensive to administer. As a result of these reasons, several cities worldwide have adopted a variety of car restriction schemes that try to reduce air pollution and congestion as well, instead of establishing congestion charges. The effects of driving restrictions in the short term are positive, but in the medium and long term, there are unwanted consequences. However, by giving private car drivers the option to pay a toll so that their cars are exempted from the restriction, these perverse incentives could be eliminated. This paper analyzes the impacts of internal factors related to individuals' attitudes and perceptions about the travel behavior of car users affected by car restriction policies in urban areas. We designed a stated preference survey conducted among car owners in Cali, Colombia, where a License Plate Restriction Charging (LPRC) policy has been in place since January 2017. Through hybrid discrete choice modeling, we demonstrated that latent variables, such as the feelings of being in control, independence, and higher social status, positively influence the decision to use cars for daily trips, thus impacting the urban modal split. The heterogeneity captured through these latent variables allowed us to understand more deeply how individuals deal with the LPRC policy in order to travel to their destination.

Simulating the Impact of Shared Mobility on Demand: a Study of Future Transportation Systems in Gothenburg, Sweden

Self-driving cars enable dynamic shared mobility, where customers are independent of schedules and fixed stops. This study aims to investigate the potential effects shared mobility can have on future transportation. We simulate multiple scenarios to analyze the effects different service designs might have on vehicle kilometers, on the required number of shared vehicles, on the potential replacement of private cars, and on service metrics such as waiting times, travel times, and detour levels. To demonstrate how simulation can be used to analyze future mobility, we present a case study of the city of Gothenburg in Sweden, where we model travel demand in the morning hours of a workday. The results show that a significant decrease of vehicle kilometers can be achieved if all private car trips are replaced by rideshare and that shared vehicles can potentially replace at least 5 private cars during the morning peak.

Time Window Delineation for Shared Parking in Urban Center

The key to solving parking problem in urban centers using shared parking strategy lies in parking demand prediction and time window delineation for shared parking spaces. By analyzing the temporal distribution of parking demand for different land types, the influence of public transport on the demand for private car trips, and the analysis of residents’ behavior of parking sharing, a behavioral choice-parking generation rate prediction model for shared parking demand is constructed. And on this basis, the study also lays emphasis on the principles and methods of parking time windows, predicts the number of available parking spaces and reserved parking spaces during the parking sharing period, and identifies the time windows for shared parking in the urban center.

Shifts in perspective: Operational aspects in (non-)autonomous ride-pooling simulations

On-demand ride-pooling systems have gained increasing attention in science and practice in recent years. Simulation studies have shown an enormous potential to reduce fleet sizes and vehicle kilometers traveled if private car trips are replaced with ride-pooling services. However, existing simulation studies assume operation with autonomous vehicles, with no restrictions on operational tasks required when the vehicles are operated by manual drivers. In this article, we simulate and evaluate the operational challenges of non-autonomous ride-pooling systems through driver shifts and breaks and compare their capacity and efficiency to autonomous on-demand services. Based on the existing ride-pooling service MOIA in Hamburg, Germany, we introduce shift and break schedules and implement a new hub return logic to perform the respective tasks at different types of vehicle hubs. This way, currently operating on-demand services are modeled more realistically and the efficiency gains of such services through autonomous vehicles are quantified. The results suggest that operational challenges substantially limit the ride-pooling capacity in terms of served rides with a given number of vehicles. While results largely depend on the chosen shift plan, the presented operational factors should be considered for the assessment of current operational real-world services. The contribution of this study is threefold: From a technical perspective, it is shown that the explicit simulation of operational constraints of current services is crucial to assess ride-pooling services. From a policy perspective, the study shows the operational challenges of a ride-pooling service with non-autonomous vehicles and the potential of future autonomous services. Lastly, the paper adds to the literature a practical ride-pooling simulation use case based on observed real-world demand and shift data.

Understanding the Shortest Route Selection Behavior for Private Cars Using Trajectory Data and Navigation Information

Traffic information and driving preference play critical roles in the route selection of drivers and further impact transport management in practice. Some studies have explored the difference between actual and shortest paths for private cars during route selection. However, the quantification of the difference and deviation as well as the impacts of the date on route selection is still seldom investigated. The study proposed a method to quantify the deviation between actual and shortest paths based on big trajectory data and the digital map. Firstly, the rules of private car travel are determined according to the definition of a trip, and the travel trajectory is divided based on these rules to attain many trips. Then, the trip routes and their attributes are generated by geographical information methods. Baidu Map’s path planning collects the shortest routes with the optimal distance and time, and the deviation between actual and recommended paths is compared. Finally, the results of 2860 private car trips of nearly 400 drivers in Chongqing, China, reveal that only about 67% of the actual trips match well with the shortest path, which was significantly higher compared to existing studies. However, the deviation between the actual and shortest paths is limited to 9 minutes or 2 kilometers. There was no significant difference between the weekday and weekend in the proportion. Compared with the weekday, the indicators of the weekend are more deviated. Path selection and the deviation vary in travel modes, OD types, drivers’ preferences, travel time intervals, and distance intervals.

GPS data Mining at Signalized Intersections for Congestion Charging.

Nowadays more private car trips have caused worse congestion due to the Covid-19 pandemic in many cities. Congestion charging is one of the taxes that is levied on vehicle owners to reduce urban traffic congestion. One of the most important reasons congestion charging is not accepted by the public is the high cost. Monitoring the state of traffic congestion in real time requires a lot of expensive installations. The purpose of this paper is to make congestion charging more accurate and acceptable using artificial intelligent algorithm. Massive real-time Global Positioning System (GPS) data provides new data for road congestion charging. The queuing length at intersections is an important measurement for the degree of traffic congestion, and it is also the basis for road congestion pricing. GPS positioning cannot provide sufficient position accuracy for lane identification of vehicles. In this study, a comprehensive model consisting of a real-time lane identification model and a real-time queue length estimation model is developed based on the traffic shockwave theory using GPS data. The comprehensive model can identify the lane where the queuing vehicle is located and estimate the real-time queue length of the lane. The proposed models were evaluated using field-collected data in Guangzhou, China. The testing results show that the proposed comprehensive model can identify lanes and estimate queue lengths with satisfactory accuracy. The model proposed in this paper provides real-time data for road dynamic pricing in a cost-effective way, which can promote the implementation of congestion charging in cities.

On the scalability of private and pooled on-demand services for urban mobility in Amsterdam

ABSTRACT The emergence of on-demand transport services is set to change the mobility landscape in urban areas. This study investigates the potential scalability of an on-demand mobility system to substitute motorised trips performed by private cars and public transport in Amsterdam, The Netherlands. We adopt an agent-based simulation framework and investigate scenarios where either private and pooled on-demand services replace private car trips, public transport trips, or both private car and public transport trips. Service performance in terms of level of service offered and operational efficiency are analysed. Results indicate that pooled on-demand services fare better than private on-demand in terms of veh-km travelled and the empty drive ratio. Private on-demand services generate 43%, 38%, and 44% more veh-km than pooled on-demand services when on-demand services replace car trips, public transport trips, or car and public transport trips, respectively. However, private on-demand services offer shorter total travel times than pooled on-demand for all scenarios.

Estimation of trip generation rates for residential land use in Uyo, Nigeria

Change in trip rates affects a transportation system and could lead to the redesign of the transport infrastructure in order to satisfy the new demand. This study estimates trip generation rates for residential land use in Uyo using cross classification method. Five (5) residential estates were considered and household survey carried out to collect trip data from 500 households on purpose and mode of travel through household interview and their response recorded in questionnaire. Four independent variables (household size, household income, car ownership, number of employed persons) were used for the study based on the prevailing conditions of theresidential land use. Cross-classification trip rates were developed from the most significant variables; household size, household income and car ownership. The analysis indicated that work trip produced the highest reported trip rates of 29.6% followed by religious trip of 24.7%. Similarly, private car trips contributed 42.8% of trips made by mode of travel as the highest trip. It was found that household size is the strongest socio-economic variable that influence trip generation in residential land use in Uyo. The cross-classification trip rates developed in this study could provide basis for the estimation of trip generation in residential land use in Uyo.&#x0D; Keywords: Trip generation; analysis of variance, ANOVA; cross classification

Estimation of optimum supply of shared cars based on personal travel behaviors in condition of minimum energy consumption

This study developed a method to determine the optimum supply of shared cars according to personal travel behaviors in the condition of minimum energy consumption. We applied the method in Southern Higher Education Zone (SHEZ) of Ningbo based on the survey data of personal travel behaviors in the region. Solutions in the case of minimum energy consumption encourage more shared-car trips, less private-car trips, and mass-transit trips. Total of 111 kJ of energy could be saved by each student from the actual case to minimum energy case. In SHEZ, 76 shared cars are recommended to provide an optimal supply of shared cars compared to 87 shared cars in reality. According to the findings, two policy implications are put forward from the aspects of supply and demand. First, to make an efficient supply, companies should focus on the distribution of shared cars according to the temporal and spatial distribution of usage, rather than only number of vehicles. Meanwhile, it is important to increase the usage of shared cars through policies, such as campaigns that raise residents’ awareness of car sharing as a green, flexible, and all-distance travel option for both workdays and holidays, and countermeasures that promote the usage of shared cars by key groups such as non-car owners. This study not only provides a method to estimate the optimum supply from viewpoint of minimum energy consumption, but also provide policy implications to guide the behaviors of both companies and users to improve the efficient usage of shared cars in Ningbo real case.

Lessons from Implementing a Metropolitan Electric Bike Sharing System

Electrically assisted bicycles are anticipated to become an effective tool to limit not only the use of cars in cities but also their negative impact on health, the environment, and passenger transportation in cities. In this paper, we examine the effects of implementing the first fully electric bike (e-bike) sharing system in the Metropolitan Area of Gdańsk–Gdynia–Sopot in Northern Poland, where no other bike sharing system had been introduced before. The aims of this article were to determine the impact of the new e-bike sharing system on the modal choice of citizens, identify barriers to its usage, and find differences between the usage of the system in the core of the metropolitan area and in the suburbs. We used two primary data sets: the survey data collected using the computer-assisted personal interviewing technique (CAPI technique) and the data automatically acquired from the website that monitored the system activities. We performed the analysis by using nonparametric tests and correspondence analysis. We found no evidence suggesting that e-bike sharing can replace large number of private car trips, but we found it likely to be competitive to carsharing, moped, and taxi services. E-bike sharing competes also with public transportation services, but it is also used as the first/last mile of the transportation supplementing public transport system. The major barrier to using this system in central cities of the metropolitan area was the lack of available public bikes, and possession of private bicycles, whereas for residents of the suburbs, the obstacles were the need to transport children, the high price of the bicycle rental/subscription, and the long distance to the docking stations.

Survey of E-scooter users in Vienna: Who they are and how they ride

In many cities around the world, electric (e-)scooters have emerged as a new means of transportation. They are often advertised as supporting modal shift towards more sustainable transportation and as a tool for enabling more equity in mobility. However, the environmental impact depends on how they are used and what kinds of trips they replace. Integration of e-scooters into urban transport systems also implicates discussions on fair road space allocation. In our study, we assess the socio-economic profiles and usage patterns of e-scooter users in Vienna, Austria. We differentiate between two basic groups of e-scooter users (renters and owners) and apply two different methods. Firstly, based on an online survey, we examine the age, gender and education of e-scooter users and we look into which kinds of trips (commuting, shopping or leisure) and which other means of transportation are replaced by e-scooter trips. Secondly, we analyse data from field observations at cycle paths in Vienna in order to determine the share of e-scooter riders and their gender distribution. We find that e-scooter users are more likely to be young, male, highly educated and residents of Vienna. According to the survey, there are considerable differences in usage between owners of private scooters and users of sharing schemes. Whereas in both groups, e-scooter trips mostly replace walking and public transport as a mode, e-scooter owners also show a considerable mode-shift from private car trips. These results implicate that e-scooter riders are additional users of cycling infrastructure. This puts further pressure on the current allocation of road space, which provides little space for active modes of transport. We conclude that city policies should address this competitive relationship adequately by allocating more space to safe and convenient cycling infrastructure and traffic-calmed zones. This could not only help ease the current challenges due to e-scooters but also provide better conditions for walking and cycling and thereby at the same time contribute to a more sustainable and equitable urban transport system.

Implications of attending the closest school on adolescents’ physical activity and car travel in Dunedin, New Zealand

BackgroundHome-to-school distances and the need for students to be driven to/from school may limit adolescents' physical activity levels. School choice and school zoning policies can influence rates of active transport to/from school, and traffic volumes during school commuting times. This study aimed to quantify the effects of adolescents' enrolment in the closest school or not, on private vehicle use and adolescents’ physical activity levels in Dunedin city, New Zealand. MethodsNew Zealand Travel Survey data (2003–2013; 2493 Dunedin respondents) were analysed to examine school-travel related private motorised trips. Dunedin-specific data from adolescents (from six Dunedin non-integrated public schools without school zoning) were analysed including self-reported survey data on school travel modes (797 adolescents), distance to the current and closest schools (797 adolescents) and accelerometer-measured moderate-to-vigorous intensity physical activity (MVPA) (121 adolescents). ResultsDuring the school commute time (8–9 am and 3–4 pm), an estimated 11.5% of car trips (95% CI 7.8%–16.8%) and 12.5% of car distance driven (95% CI 8.8%–17.7%) were related to secondary-school travel with approximately half of these involving trip-chaining. Compared to adolescents living beyond cycling distance (>4 km) from their current school, adolescents living within walkable distance (≤2.25 km) and cyclable distance (>2.25-≤4 km) accumulated an additional 12.1 min (95% CI 7.0 to 17.2) and 6.1 min (95%CI 1.0 to 11.2) of daily MVPA during the school commute time, respectively. If adolescents currently enrolled at a distant school were to attend the closest school, they could accrue an additional 3.4 min MVPA during the 2-h school commute time period and school travel-related private car trips would be reduced by 7%. ConclusionsIn Dunedin, New Zealand, modest reductions in private vehicle traffic, particularly around schools, and increases in adolescents’ MVPA during the school commute period would be expected if all adolescents attended the closest school.

Appraising an incentive only approach to encourage a sustainable reduction in private car trips in Dublin, Ireland

The research presented in this paper examines the potential for a reduction in car use in the Greater Dublin Area (GDA) as a result of introducing a range of sustainable policy incentives. A package of mode-specific incentives devised to encourage a mode shift away from single occupancy vehicle (SOV) use to walking, cycling, public transport, carpooling and car-sharing were examined for their potential to reduce car mode share. The policies tested were represented in a four-stage transportation model to generate new mode choices and subsequently new mode shares. Changes in mode share were similarly analyzed in terms of tailpipe emissions savings from changes in SOV kilometers traveled and estimations of the potential monetary savings accrued from a reduction in emissions. Ultimately, an indication of possible reductions in private car use and the potential for a rise in sustainable mode choice and a reduction in car trips was ascertained in the GDA. Modeling was conducted in collaboration with the National Transport Authority (NTA) of Ireland to represent the policy changes by means of parameter modifications in the Mode Choice and Trip Assignment stages of the Eastern Regional Model. These changes were made to account for modifications made to infrastructure, frequency, time and cost parameters. A 2012 Base Year and forecasted 2035 scenario were considered in this study. Outputs from these modeled scenarios produced mode share estimates derived from new origin-destination trip demand and mode choice predictions. The results showed that the policy interventions tested incentivised more mode shifting behavior among sustainable modes rather than from private car trips to public transport and active mode trips. Moreover, it was estimated that active mode users were more elastic or sensitive to changes made in the pedestrian network than the cycling network, which is reflected in mode shifting from cycling to walking. A reduction in the mode share of private cars of up to 1.6% and an increase in walking of up to 5% were estimated across the modeled scenarios in the GDA, and a daily monetary saving of up to €14,125 could be achieved as a result of reductions in CO2, NOx, and PM2.5 private car emissions from the introduction of the policy incentives tested. Overall, this study assesses the behavioral impacts of introducing a range of policy incentives alone on private car use and produces evidence to support the effectiveness of a carrot and sticks approach versus an incentive only method.

Influences of rural built environment on travel mode choice of rural residents: The case of rural Sichuan

China has entered a stage in which new rural construction and urbanization are rapidly developing. Considerable changes are occurring in rural China, and the built environment is different from that in the past; such difference directly influences the travel mode choice of rural residents. However, our knowledge on how the rural built environment influences the travel mode choice of rural residents in China remains limited. To fill this gap, this study combines on-site measurement methods, geographic information system (GIS) technology, and activity diary survey to obtain basic data regarding the built environment and the daily activities of rural residents. The multinomial logit (MNL) model is used to explore the relationship between the rural built environment and the travel mode choice of rural residents. Results show that building density significantly positively affects private car trips. This finding challenges earlier urban built environment research due to the considerable gap between rural and urban areas. An increase in road density increases the travel frequency of electric bicycles and motorcycles. Accessibility perception and preferences positively affect the probability of choosing to walk. Safety and neighborhood harmony perception positively affect the travel frequency of motorcycles and private cars. Rural residents who prefer a safe living environment are likely to choose walking for their daily travel. Despite the considerable achievements in the construction of rural roads, the frequency of public transportation remains low for rural residents. Therefore, additional attention should be given to the investment and construction of public transport facilities during rural urbanization.

Understanding car sharing preferences and mode substitution patterns: A stated preference experiment

Car sharing is an emerging mode of transport that enhances the mobility of individuals. However, evidence regarding how car sharing affects travel behavior is still limited. This paper analyzes how individuals value various characteristics of car sharing services and substitution patterns between car sharing and traditional transport modes (private cars, public transport and bike). We present the results from a stated preference mode choice experiment conducted in Copenhagen. The design of the choice experiment is pivoted with respect to revealed trips from respondents in order to imitate realistic choice situations. The survey is distributed online primarily within a university environment to facilitate oversampling of young and educated individuals, who are more likely to consider car sharing as a mode of transport. We analyze the data using a mixed logit model and derive the willingness-to-pay for vehicle reservation, parking availability and convenient access to car sharing vehicles. The results show that willingness-to-pay measures for these characteristics are relatively high and could have an important effect on car sharing adaption for the segment represented by our sample. In addition, results show that free-floating car sharing is a strong competitor of public transport and bike trips and to a lesser degree of private car trips.

Hub network design for integrated Bike-and-Ride services: A competitive approach to reducing automobile dependence

The accumulation of private automobiles in urban transportation networks has become a serious challenge in many developing countries. However, introduction of a competitive mode could dissuade the passengers from commuting by car and hence alleviate traffic congestion and its consequent environmental impacts. In the present study, we address this issue by proposing an innovative mathematical model that maximizes the competitiveness of Bike-and-Ride (B&R) services in inter-zonal trips, where a stand-alone Bicycle Sharing System (BSS) is uncompetitive against the car. Considering both access and egress ends of trips for the first time in the literature, the model optimally lays out the B&R network elements (a hierarchical set of BSS stations referred to as hubs, and their linking bicycle lanes) under budget constraints. Having selected the city of Isfahan as a case to implement our model on, we conduct experimental and sensitivity analyses to gain further insights into how different parameter settings affect the objective value. Results indicate significant modal shifts (from private automobiles toward B&R) occur in solution networks yielded with limited budgets available. This confirms that unlike the existing BSSs that are shown to mainly substitute public transportation, if designed as a feeder mode with the objective of cutting private car trips, a BSS could be significantly effective in boosting public transportation ridership through attracting demand from private cars to B&R. With value of time taken as an indicator of the passengers’ income, it is found that affluent societies will be less likely to substitute their car for the B&R mode if the latter is the slower mode overall, and that the model fares best for developing countries. It is worth mentioning that these findings are based on the assumption of temporal and monetary expenses being the only determining factor in mode-choice.

Car4Pac: Last Mile Parcel Delivery Through Intelligent Car Trip Sharing

The explosion of online shopping brings great challenges to traditional logistics industry, where the massive parcels and tight delivery deadline impose a large cost on the delivery process, in particular the last mile parcel delivery. On the other hand, modern cities never lack transportation resources such as the private car trips. Motivated by these observations, we propose a novel and effective last mile parcel delivery mechanism through car trip sharing, to leverage the available private car trips to incidentally deliver parcels during their original trips. To achieve this, the major challenges lie in how to accurately estimate the parcel delivery trip cost and assign proper tasks to suitable car trips to maximize the overall performance. To this end, we develop Car4Pac , an intelligent last mile parcel delivery system to address these challenges. Leveraging the real-world massive car trip trajectories, we first build up a 3D (time-dependent, driver-dependent and vehicle-dependent) landmark graph that accurately predicts the travel time and fuel consumption of each road segment. Our prediction method considers not only traffic conditions of different times, but also driving skills of different people and fuel efficiencies of different vehicles. We then develop a two-stage solution towards the parcel delivery task assignment, which is optimal for one-to-one assignment and yields high-quality results for many-to-one assignment. Our extensive real-world trace driven evaluations further demonstrate the superiority of our Car4Pac solution.

Measuring the potential emission reductions from a shift towards public transport

The research presented in this paper investigates the behavioural response and subsequent appraisal of a range of policy incentives devised to encourage greater use of sustainable travel options within the Greater Dublin Area (GDA). Interventions were assessed by employing four-stage modelling techniques, as a tool to identify signs of mode shifting behaviour from single occupancy vehicle (SOV) use to public transport modes. Extensive modelling work was conducted in collaboration with the National Transport Authority (NTA) of Ireland utilising the National Regional Modelling System (RMS) to represent the sustainable policy changes. Parameter modifications in the mode and destination choice and trip assignment stages were made to account for improvements made to frequency, time and cost attributes of public transport modes included in the model. Subsequent changes in vehicle kilometres travelled of various modes modelled were then utilised to estimate the potential emissions savings. In addition to this, the potential monetary savings generated from a reduction in emissions were calculated. The key results produced from this study found that an increase in the mode share of public transport of up to 2.87% could be achieved in the GDA. Up to 1.76% of which was estimated to come directly from private car trips, as a result of implementing the policy incentives tested. Furthermore, daily emissions savings of up to 431.58 tonnes of CO2, 0.95 tonnes of NOx, and 0.046 tonnes of PM2.5 were also estimated.