Exploring the Impact of Public Transport Strikes on Travel Behaviour and Traffic Congestion

Nguyen-Phuoc, Currie, De Gruyter, Young 0 0 0 0 0 Title Page Exploring the Impact of Public Transport Strikes on Travel Behaviour and Traffic Congestion Duy Q. Nguyen-Phuoc* Public Transport Research Group, Institute of Transport Studies Monash University Victoria, Australia 00 Tel: + 0; Email: nguyen.duy@monash.edu Graham Currie Public Transport Research Group, Institute of Transport Studies Monash University Victoria, Australia 00 Tel: + 0; Email: graham.currie@monash.edu Chris De Gruyter Public Transport Research Group, Institute of Transport Studies Monash University Victoria, Australia 00 Phone: + 0 Email: chris.degruyter@monash.edu William Young Department of Civil Engineering, Monash University Wellington Road & Blackburn Road, Clayton, VIC, 00 Tel: + 0; Email: bill.young@monash.edu *Corresponding Author Submitted for presentation only Words:, text + ( Tables and Figures * 0=,0) =, (limit=,00) Committee: AP0 Public Transport Planning and Development First Submission date: Thursday, July 0 Second Submission date: Publication Submission date: NOT APPLICABLE

Nguyen-Phuoc, Currie, De Gruyter, Young 0 ABSTRACT This paper explores the network-wide impacts of withdrawal to public transport (train, tram and bus) on traffic congestion in Melbourne, Australia. A primary survey which aimed to investigate the mode shift of users when each public transport mode ceases in the short term was conducted with public transport users in May 0. The findings showed that train withdrawal are expected to result in % of users shifting to car. Smaller yet significant shifts to car are also expected with bus withdrawal (%) and tram withdrawal (%). Based on the survey results and the use of a four-step transport model, train withdrawal are expected to increase the number of severely congested road links by 0% and reduce the average travel speed from km/h to km/h (0% decrease). Bus and tram withdrawal are also found to increase congestion although the results are less severe. Future research should look to adopt a larger sample size and explore the longterm effects of public transport withdrawal. Abstract = words (limit = 0 words) Keywords: Public transport, network-wide, congestion, withdrawal, transport model.

Nguyen-Phuoc, Currie, De Gruyter, Young 0 0 0 0 0 INTRODUCTION Traffic congestion is a major issue in the daily lives of commuters, especially those living in big cities. As the number of vehicles on the road network grows, congestion has an increasing direct effect on commuters. For example it is expected that congestion costs in Australian cities could be over $0 billion AUD by 00 (). In order to reduce the effect of traffic congestion, Public Transport (PT) offers a method of increasing person throughput. In Melbourne, PT carries over % of travel and this figure is expected to increase in the future (). The value of public transport in terms of traffic congestion relief is often graphically demonstrated when strikes causing a withdrawal of public transport occur. The withdrawal of an entire PT system or an individual PT mode can be expected to have significant effects on traffic congestion as a share of PT users may shift to car (-). Other users could switch to non-motorised modes such as cycling or walking. For those who cannot find an appropriate alternative mode, they might cancel their trips. In the event of a PT mode withdrawal, a number of PT users would be expected to switch to other PT modes that still operate. This mode shift would put pressure on other PT modes as the number of passengers suddenly increases during withdrawal. PT service withdrawal can result from a variety of factors including malfunctions and breakdowns, power outages and labour strikes (). Recently, PT strikes have occurred more frequently in several large cities around the world. For example, in 0, Melbourne s public transport system experienced tram and train strikes, as unions negotiated working conditions. However, as strikes only occur on an irregular basis, only limited research has explored the effects of PT strikes on travellers (). In addition, since strikes can demonstrate how PT acts to relieve congestion, we only have a limited understanding of these benefits due to the limited research in this area. Thus, it is important to investigate the behavioural reaction of users to PT withdrawal and their effects on transit and road traffic congestion. Based on the findings, appropriate remedial actions can be proposed and implemented to better mitigate the impact on the transport system. For example, in The Hague, the government allowed travellers to park on downtown bus lanes and tramways in the event of a PT strike. In New York, on-street parking was banned to increase road capacity through the city during a strike (). This paper explores how PT users change their travel behaviour if individual PT modes cease in the short term. The network-wide impact of each PT mode withdrawal on traffic congestion is also investigated. The paper is organised as follows: a review of available research regarding the behavioral reactions of PT users when PT withdrawal occurs. Research on impacts on traffic are then presented. This is followed by a description of Melbourne s PT system as the context for this research. The research methodology is presented. Results are then described. The final section concludes with major findings, policy implications and makes suggestion for further research in this area. BACKGROUND Although the withdrawal of PT, particularly PT strikes, has occurred more frequency in recent years (), studies in this area are very limited. Exel and Rietveld () reviewed studies of PT strikes between and 000 in Europe and the United States to explore the behavioral reaction of PT users. The impact of PT strikes varies depending on the type of strike, usual travel patterns and the policy response. They found that when PT ceases, PT users would switch to car (ranging from 0% % of PT users), switch to other modes (% - %) or cancel their trips (% - %). Exel and Rietveld () carried out secondary analysis on data collected from people who had planned to travel by train on the day of a national rail strike in the Netherlands in 00. The main purpose of their study was to explore the behavioural reactions of train travellers to a rail strike and investigate the characteristics of travellers and trips that affect the chosen alternative. They found that % of train travelers shifted to car as a driver, % shifted to another mode and % decided to reschedule their trips to another day. Overall, % of trips were cancelled on that day. Factors affecting the behavioural reactions of PT users in the event of a PT withdrawal have also been explored in a number of studies (; ). Other studies have assessed the impact of PT strikes on traffic congestion. Lo and Hall () explored the impact of PT strikes that took place in Los Angeles over a -day period in October and November 00. Traffic conditions during the strike were measured to understand how PT affects congestion experienced by car drivers. They measured the traffic speed on freeways before and after the strike by using various

Nguyen-Phuoc, Currie, De Gruyter, Young 0 0 0 0 0 sensors. They found that there was a traffic speed decrease of 0% during the strike. Anderson () explored whether PT strikes generate a much larger congestion impact than earlier estimates using a choice model and data from a sudden strike in 00 by Los Angeles PT workers. A regression discontinuity design was used to calculate the travel delay if PT is not available. He found that the average highway delay would increase by % during peak hours when PT ceases, particularly adjacent to rail corridors (). This indicates that high quality, grade-separated services (such as trains) are considerably effective at reducing congestion. Similarly, the research of Laval et al. () pointed out that there were severe traffic problems on roads when a disruption of the Bay Area Rapid Transit (BART) system in San Francisco occurred. The absence of BART services on major East Bay corridors would generate morning traffic queues stretching miles with mi/hr speeds, and afternoon queues stretching miles with mi/hr speeds. More recently, Moylan et al. (0) investigated the impacts of rapid transit in the San Francisco Bay Area region on roadway travel demand and travel time when PT services are suspended during a strike. In order to estimate the lower bound of the impact, they compared traffic volumes, which were collected from a system of,000 buriedloop-detector stations on freeways during the strike, against observations from the same time and day of week throughout the year. In contrast, the upper bound of the impact was measured using an experiment. They assumed that all PT users with access to a car would shift to driving alone. A non-parametric modelling technique was then used to compare the travel time distributions associated with the traffic volume and travel demand. They found that at the network level, the impact of the BART strike was not significant. However, on roads running parallel to PT services, there were significant delay, particularly in the peak periods. Morning peak conditions on a parallel road (Highway ) were nearly at the 0th percentile of annual volume-weighted travel times. Aftabuzzaman et al. () explored the impacts of individual PT modes (train, tram and bus) on traffic congestion relief in Melbourne. They used a four-step transport model and assumptions relating to the diversion of PT users to car when PT is removed. From secondary research, they suggested that on average % of PT users would shift to car if PT was not available. This fixed value was also applied for individual PT modes if they were separately removed. The modelling was based on removing the PT system from the network and reallocating of transit trips to car travel. They found that Melbourne s train operations have the greatest impact on congestion relief across all suburbs, reducing the number of congested links by %. This was followed by bus and tram which reduced the number of congested links by % and % respectively. This paper builds on the analytical process used by Aftabuzzaman et al. () in estimating the impact of individual PT mode withdrawal on traffic congestion. However, this research incorporates the results of a field survey of PT users to determine how mode shift to car would vary in different parts of the city (inner, middle and outer). This method is considered to be more precise as changes in travel behaviour are accounted for spatially (). RESEARCH CONTEXT Melbourne s PT system Melbourne has a population of. million people over nearly,000 km. The Central Business District (CBD) plays a dominant role for many forms of retailing, employment and recreation. Like other cities in Australia, Melbourne has a high dependency on the automobile with a total of. million private vehicle trips per day. Melbourne has an integrated public transport system that extends from the city centre in all directions, with trains, trams and buses offering comprehensive PT services. The PT system in Melbourne carries % of all trips within the metropolitan area, or % when expressed in terms of passenger kilometres (). As shown in Figure, Melbourne s PT system consists of train, tram, and bus services. Melbourne's metropolitan train network consists of lines with a total length of km (track length of 0 km) (). The network is primarily at-grade, with more than 0 level crossings. Melbourne s train network carried more than 0 million passenger trips per year (). Tram is a major form of public transport in Melbourne, with 0 kilometres of tram track and routes. It is the largest urban tramway network in the world (). Tram is the second most used form of public transport in Melbourne after the commuter railway network, with million passenger trips per year (). The majority of the tram network is located in the inner city area.

Nguyen-Phuoc, Currie, De Gruyter, Young Melbourne has a total of bus routes carring over million passenger trips per year (Currie, 0). While the city relies on a radial train network and inner city tram network, the outer suburbs are primarily serviced by bus. Buses normally operate in mixed traffic conditions although there are several exclusive bus lanes provided for premium bus services. 0 FIGURE Public transport network in Melbourne. Spatial unit of analysis Local Government Areas (LGAs) are the base unit of analysis used in this study. There are LGAs in Melbourne () which are grouped into three categories: inner ( LGAs), middle ( LGAs) and outer ( LGAs). These are shown in Figure.

Nguyen-Phuoc, Currie, De Gruyter, Young 0 0 FIGURE Local Government Areas (LGAs) in Melbourne. STUDY METHODOLOGY This section describes the methodology developed to estimate the impact of individual PT mode withdrawal on traffic congestion. Firstly, the method used to estimate mode shift to car is presented. Secondly, the method used for assessing the network-wide impact of PT withdrawal on traffic congestion is described. Primary research An online survey of PT users across metropolitan Melbourne (inner, middle and outer) was conducted in April 0. The aim of the survey was to understand the behavioural reaction of PT users in the event of a PT withdrawal. Respondents who used PT in the weekday morning peak were asked about the impact of individual PT mode withdrawal and their likely change in travel behaviour. Firstly, an email was sent to all members of a market research panel inviting them to take part in the study by answering an on-line questionnaire. In the email invitation, each panel member was given a link to access the questionnaire. A reminder email was sent to those who had not accessed the questionnaire one week after the initial email was sent. Data was collected over a -week period during autumn and therefore reflected autumn travel behaviour. A total of, people accessed the survey in which respondents (.%) passed a screening process. Out of the respondents, users confirmed that they used the train, users used the tram and users used the bus for their last PT trip (some travellers used multiple PT modes). These respondents were asked to describe their behavioural reactions in the event of each PT mode closure. From the results of the survey, the share of mode shift to other travel modes for inner, middle and outer areas could be estimated. This research has assumed that PT user diversion to car when each PT mode ceases would have an impact on traffic congestion. It is clear that the mode shift to a car as a driver would directly increase the number of car trips on the road network (diversion to walking or cycling is not considered to directly influence congestion). However, in the case of switching to a car as a passenger, this may or may not

Nguyen-Phuoc, Currie, De Gruyter, Young 0 0 0 0 influence traffic congestion. For example, Litman () argued that some car users can spend a significant amount of time driving children to school, family members to work and elderly relatives on errands (chauffeuring trips). These trips can be particularly inefficient if drivers are required to make an empty return trip which can contribute to congestion. For the purpose of this modelling analysis, it is assumed that half of all car passenger trips involve chauffeuring (). Thus, the car mode shift share contributing to traffic congestion if PT operations cease would be the sum of the share of mode shift to car as driver and a half of the share of mode shift to car as passenger. Modelling the impact of individual PT mode withdrawal A modelling procedure was developed to explore the impact of individual PT mode withdrawal on traffic congestion. The procedure adopted an assumption regarding PT user diversion to car and a conventional four-step transport model (the Victorian Integrated Transport Model, or VITM). The modelling analysis was carried out for weekday morning peak (am am). VITM is a conventional four-step transport model used to estimate travel demand in the Australian state of Victoria. The model is implemented in a Cube software platform. In VITM, the road network is represented by a set of links (, links) and nodes, divided into, zones. Nodes usually represent an intersection or a change in road characteristics, while links represent the segments of actual roads in the network. VITM contains a number of sub-models which work together to create the required output for each link such as speed, volume and travel time. The modelling undertaken in this research includes the following major steps (Figure ): In the scenario of Base case : Identify the level of congestion on the road network with the operation of all PT modes. Estimate the PT demand for each zone (train trip matrix, tram trip matrix and bus trip matrix) by conducting a PT assignment process. In the scenario of Train withdrawal : Determine the additional car trips caused by train service withdrawal (multiply the train trip matrix by the share of mode shift to car for inner, middle and outer areas). Add the additional car trip matrix to the existing car trip matrix to create a new car trip matrix that represents the impact of train withdrawal. Assign this new car matrix on to the road network to estimate the level of congestion after train operations are removed. A comparison of the level of congestion between the two scenarios, Base case and Train withdrawal, is undertaken to understand the effect of train withdrawal on traffic congestion. The impacts of tram and bus withdrawal were estimated using a similar process. In the event of a train closure, the existing 0 at-grade rail crossings are assumed to have no impact on traffic flow. This is considered to present the positive impact of train withdrawal. However, the results of research conducted by Nguyen-Phuoc et al. (0) show that the network-wide impact of at-grade rail crossings on traffic is not significant. Similarly, it is assumed that the negative effect of bus operations on traffic flow is relatively small. Thus, in this study these effects are not considered. Regarding the effect of tram withdrawal on traffic, Nguyen-Phuoc et al. () found that tram operations have both positive and negative effects on traffic congestion. The negative effects of trams include the impact of low tram speeds, the impact of curbside tram stops on non-exclusive tram rights-of-way, and the occupation of priority tram lanes on semi-exclusive tram rights-of-way. If tram withdrawal occurs, the impacts of low tram speeds and curbside tram stops on traffic flow are removed. Thus, when modelling the impact of tram withdrawal, the approach needs to consider both the positive and negative effects of trams. This methodology was initially developed by Nguyen-Phuoc et al. ().

Nguyen-Phuoc, Currie, De Gruyter, Young V0: traffic volume from mode choice model V: updated traffic volume TT0: travel time on links (without trams) TT: travel time on links with exclusive tram rights-of-way % change: Percentage change in traffic travel time caused by tram operations F: tram frequency β: the share of mode shift to car from PT users for inner, middle and outer areas (%) Traffic volume Vo (Car trip matrix) PT demand (PT trip matrix) Microsimulation % change = f(v,f) Estimate time varying PT service travel time Estimate time varying link travel time TT = TT 0 + % change x TT 0 Equilibrium Equilibrium Find time dependent shortest path and auxiliary flow Find PT service path based on the shortest path and lowest fare (a) Base case Update vehicle path assignment (V) Update PT service path assignment Stopping criterion if travel time are equal for all used paths Train demand (Train trip matrix) Tram demand (Tram trip matrix) Bus demand (Bus trip matrix) Estimate congestion measures based on car demand Traffic assignment (Macro-modelling) Public transport assignment Traffic volume Vo (Car trip matrix + β%* Train/Bus trip matrix) Microsimulation % change = f(v,f) Estimate time varying link travel time TT = TT 0 + % change x TT 0 Equilibrium Find time dependent shortest path and auxiliary flow (b) Train/Bus withdrawal Update vehicle path assignment (V) Stopping criterion if travel times are equal for all used paths Estimate congestion measures based on car demand Traffic assignment (Macro-modelling) 0 Traffic volume Vo (Car trip matrix + β%* Tram trip matrix) Estimate time varying link travel time (TTo) Equilibrium Find time dependent shortest path and auxiliary flow (c) Tram withdrawal Update vehicle path assignment (V) FIGURE The process for estimating travel demand in each scenario. Stopping criterion if travel times are equal for all used paths Estimate congestion measures based on car demand Traffic assignment (Macro-modelling) RESULTS AND DISCUSSION The results are presented in two parts. The findings from the field survey are presented first, followed by the results of the modelling which detail the impact of PT withdrawal on traffic congestion. Primary research Respondent characteristics A total of respondents completed the survey, comprised males (.%) and females (0.%) (Table ). The highest proportion (.%) of respondents was 0- year olds, closely followed by -

Nguyen-Phuoc, Currie, De Gruyter, Young year olds (.%) and 0- year olds (0.%). Users aged from 0 to years accounted for the lowest proportion of respondents (.%). A Chi-squared test was conducted to compare the gender and age distribution between the sample and Melbourne s PT user population from the 0 Census. The results of the chi-square test showed that there was no significant difference and that the sample is therefore representative of the broader PT user population. Characteristic Number of respondents (n) TABLE Demographic profile of respondents Survey Census * Proportion (%) Expected Value (n) Proportion (%) Chi-squared χ Gender Male.. 0.00 Female 0. 0. 0.00 0 Age -.. 0. 0-0... 0-0.. 0.0 0-0. 0. 0. 0+...00 Total 00 00 * Population with a journey to work by PT in Melbourne (0 Census) χ Gender (0.0) < χ Critical (.), χ Age (.) < χ Critical (.) The level of significance for this test is α=0.0 Table presents the use of each PT mode among survey respondents. This indicates that the highest proportion of users travelled by train (.%), followed by tram (.%) and bus (.%). These proportions are generally consistent with the analysis of PT users in the Victorian Integrated Survey of Travel and Activity (VISTA) and the modelled outcome from VITM. Given that train is the most utilised PT mode in Melbourne, the withdrawal of train services is expected to generate the largest effect on traffic congestion of all PT modes. TABLE PT mode distribution of users in Melbourne Survey VISTA Modelled in VITM No. % No. % No. % Train.,.,. Tram..,. Bus 0..,.0 0 Total *,, * Total is not 00% because a number of users travelled by multiple PT modes Figure shows the spatial distribution of the PT trip origins of survey respondents. Train trips are distributed across all parts of Melbourne while a high proportion of tram trips are within in inner city. Respondents travelling by bus tend to make trips from the middle and outer areas rather than in inner city.

Nguyen-Phuoc, Currie, De Gruyter, Young 0 0 0 FIGURE Distribution of PT trip origins among respondents. Mode shift Based on the survey results, table provides information about the stated behavioural reactions to each PT mode withdrawal among PT users. In the event of a train withdrawal, a relatively high proportion of train users would shift to car as a driver (.%), particularly in outer areas where the mode shift is.0%. The number of users switching to other PT modes (tram and bus) accounts for around 0% of train users in total. Non-motorised modes were chosen by less than % of train users, while.% said that they would cancel their trips. In the event of a tram withdrawal, % of tram users would switch to train, while only % would shift to bus. In the inner city, a relatively high proportion of tram users would choose to walk (.%), which is much higher than the proportion who would choose to walk in the event of a train withdrawal (.%). The number of tram users who would shift to car as a driver accounted for only %. The highest share of bus users.% would shift to car as a driver as a result of a bus withdrawal. This is followed by mode shift to train (.%) and tram (.%). Only % of bus users would choose to walk while around % would cancel their trips. Table also shows the share of mode shift to car when individual PT modes cease. Train withdrawal is expected to generate the highest mode shift to car (.%). This is followed by bus withdrawal and tram withdrawal with.% and.% respectively. These figures are substantially different for each part of metropolitan Melbourne, reflecting the traffic characteristics of those areas (). For example, in the event of a train withdrawal, mode shift to car in outer areas is nearly triple that for the inner city. In contrast, mode shift to car in outer areas is the lowest if tram operations cease, reflecting the predominance of the tram network in the inner and middle areas. These figures are used in the four-step transport model (VITM) to examine the expected changes in traffic congestion during PT withdrawal.

Nguyen-Phuoc, Currie, De Gruyter, Young TABLE Behavioural response of PT users when each PT mode ceases in the short term Behavioural reactions Train (%) (n=) Tram (%) (n=) Bus (%) (n=) Inner Middle Outer Total Inner Middle Outer Total Inner Middle Outer Total Train - - - -........ Tram... 0. - - - -.... Bus.. 0...... - - - - Car as driver...0... 0..0.... Car as passenger...........0. Taxi/uber........ 0.0... Cycle.. 0....... 0.0.. Walk...... 0...... Cancel the trip.......... 0.. Other. 0. 0. 0..... 0.0... Total 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 0 0 Mode shift to car *. 0........... * Mode shift to car = mode shift to car as driver + 0. x mode shift to car as passenger Modelling results Table reveals the impact of individual PT mode withdrawal on Melbourne s road network. A comparative assessment of all measures indicates the following: - Train withdrawal causes an increase of over 0% in the number of severely congested links while the withdrawal of trams and buses results in an increase of only.% and.% respectively. - There is an increase in the number of vehicles experiencing congestion when train, tram and bus cease (considering the impact of each PT mode separately) of.%,.% and.% respectively. - Train withdrawal increases total delay on the road network by.%, whereas tram and bus withdrawal increases delay by only % and.% respectively. - Average travel speed decreases from. km/h to. km/h as a result of train withdrawal. Similarly, the travel speed also deceases when tram or bus operations cease however the level of reduction is relatively low (0.% and.0% respectively). - When train services are not available, actual travel time per kilometre increases by nearly %. This is much higher than the increase in travel time in the event of a tram or bus withdrawal (.% and.% respectively). Overall, it can be seen that train withdrawal results in a much higher impact on traffic congestion compared to the withdrawal of tram or bus services. The impact of bus withdrawal on traffic congestion is more than double that caused by tram withdrawal. Figure illustrates the spatial distribution of congested links in the Base case scenario and Train withdrawal scenario. This shows a considerable increase in traffic congestion, particularly in the inner and middle areas. TABLE Impact of individual PT mode withdrawal on the road network Measure Number of severely congested road links (V/C>=0.) () Base case Train withdrawal Tram withdrawal Bus withdrawal Difference Difference Difference (%) (%) (%),.0,.0.,0.0.,.0.

Nguyen-Phuoc, Currie, De Gruyter, Young Number of moderately congested road links (V/C>0.) (),.0,.0.,.0.,0.0. Length of congested road links (km),.,0..,..0,.. Congested road links (%).....0 0.. Number of vehicles experiencing congestion (millions)....... Vehicle distance travelled (million veh-km).0.0..0 0... Vehicle time travelled (million veh-hr) 0. 0.. 0..0 0.. Total delay on road network (million veh-hr).....0.. Average travel speed (km/h).. -.. -0.. -.0 Actual travel time per km (min/km)....... Notes: V/C: volume to capacity ratio = traffic volume divided by road capacity a) Base case

Nguyen-Phuoc, Currie, De Gruyter, Young 0 0 b) Train withdrawal FIGURE Distribution of congested road links in Melbourne. DISCUSSION AND CONCLUSION This paper has investigated the impact of individual PT mode withdrawal on travel behaviour and traffic congestion. A literature review revealed that most studies exploring the impact of PT withdrawal on congestion have adopted simple assumptions on car diversion from PT. The share of mode shift to car has been used in a number of mathematical and simulation modelling studies to examine the effect of PT withdrawal on congestion. However, due to the lack of primary data, prior studies assumed that either all PT users would shift to car, or that a total fixed share would shift based on secondary data. These methodologies are considered limited and simplistic. In this study, a primary survey was designed to measure changes in PT user behaviour if PT ceases in the short term. The findings showed that when individual PT mode withdrawal occurs, a high proportion of users (-%) would switch to other PT modes. Other users would shift to car as a driver (-%) or a passenger (-%), taxi/uber (-%) or a non-motorised mode (-%). Only a small group (-%) would choose to cancel their trips. In the event of a train or bus withdrawal, a high proportion of PT users (-%) would switch to driving a car. This is because the majority of train and bus users have long distance trips which cannot be taken by non-motorised modes such as cycling or walking. Shifting to other PT modes is not always an appropriate alternative, particularly for users living in outer areas where PT services limited. In contrast, during a tram withdrawal, a high share of tram users (%) would switch to train and bus as trams operate mostly in inner and middle areas where other PT modes can be accessed easily. Walking is also chosen by many tram users (%) because a large proportion of trips taken by tram are within walking distance. Based on the share of mode shift to car for inner, middle and outer areas, transport modelling (VITM) was undertaken to examine the impact of individual PT mode withdrawal on traffic congestion. Overall, train withdrawal shows the highest impact on traffic congestion, followed by bus and tram withdrawal. A key reason is that train is used by the majority of PT users (approximately 0%) and the share of mode shift

Nguyen-Phuoc, Currie, De Gruyter, Young 0 0 0 0 0 to car is higher than that for tram and bus withdrawal. The increase in car trips associated with train withdrawal can therefore lead to significant impacts on traffic congestion. This is consistent with the findings of Anderson () who showed that grade-separated services (such as trains) are considerably effective in reducing congestion than other PT modes. The key contributions of this paper are: An understanding of the changes in travel behaviour among PT users when each PT mode ceases in the short term. A development of a more precise method to explore the network-wide effect of individual PT mode withdrawal on traffic congestion by considering the spatial distribution of expected mode shift to car. The results of this paper can help authorities examine the impact of PT withdrawal on traffic congestion. From this, measures can be better targeted to deal with these issues. For instance, the frequency of alternative PT modes can be increased in areas experiencing high levels of traffic congestion during PT strikes. Other policies could be proposed such as allowing vehicles to travel or park in priority bus lanes or tram lanes if these PT modes cease, thereby increasing road capacity in the short term. There are two key limitations associated with the findings reported in this paper. Firstly, the sample size from the survey is relative small, particularly for tram and bus users. Increasing the sample size in future research could lead to a better understanding of the behavioural reactions of PT users in the event of PT withdrawal. Secondly, the survey was conducted during autumn. Ideally, the survey should be carried out in different seasons so that the effect of weather on stated travel behaviour can be determined. This research has estimated the impact of PT withdrawal on congestion in the short-term. Future research could therefore look to focus on the impact of long-term withdrawal (). ACKNOWLEDGEMENTS We would like to thank the Australian Government for funding Duy Nguyen s PhD Development Scholarship since this paper has resulted from his research funded as part of this scholarship. REFERENCES. Garnaut, R. Transforming Transport.The Garnaut climate change review, 0. pp. 0-.. BITRE. Yearbook 0: Australian Infrastructure Statistical Report. Bureau of Infrastructure, Transport and Regional Economics (BITRE), Canberra ACT, 0.. Blumstein, A., and H. D. Miller. Making Do: The effects of a mass transit strike on travel behavior. Transportation, Vol.,, pp. -.. Exel, N. J. A. v., and P. Rietveld. Public transport strikes and traveller behaviour. Transport Policy, Vol., No., 00, pp. -.. Exel, N. J. A. v., and P. Rietveld. When strike comes to town anticipated and actual behavioural reactions to a one-day, pre-announced, complete rail strike in the Netherlands. Transportation Research Part A: Policy and Practice, Vol., No., 00, pp... Pnevmatikou, A. M., M. G. Karlaftis, and K. Kepaptsoglou. Metro service withdrawal: how do people choose to travel? Transportation, Vol., No., 0, pp. -.. Lo, S.-C., and R. W. Hall. Effects of the Los Angeles transit strike on highway congestion. Transportation Research Part A, Vol. 0, 00, pp. 0.. Anderson, M. L. Subways, strikes, and slowdowns: The impacts of public transit on traffic congestion. NBER Working paper series, 0.. Laval, J., M. Cassidy, and J.-C. Herrera. Traffic Impact Analysis: Effects Of The Absence Of Bart Service On Major East Bay Corridors. Institute of Transportation Studies, UC Berkeley, 00. 0. Moylan, E., F. Foti, and A. Skabardonis. Observed and simulated traffic impacts from the 0 Bay Area Rapid Transit strike. Transportation Planning and Technology, 0, pp. -.. Aftabuzzaman, M., G. Currie, M. Sarvi, and a. Modeling the Spatial Impacts of Public transport on Traffic Congestion Relief in Melbourne, Australia. Journal of the Transportation Research Board, No., 00, pp. -0.. Nguyen, P. Q. D., G. Currie, and B. Young. Public Transport Congestion Relief Measurement A

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