Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Development, practical challenges, and application of a state-wide transport model system in Australia
No full textThis paper develops a regional strategic travel model system to predict expected changes in traffic volumes and public transport patronage up to 2056. On the supply side, we develop transport network datasets for four linehaul modes – train, coach, car, and plane, incorporating travel times, fares, costs, and service frequency. On the demand side, an aggregate modal share logit model for three trip purposes is estimated to identify the role of various trip attributes and socioeconomic characteristics to forecast modal shares in the base year 2016. The resulting models produce an accessibility index asess each mode role in defining the accessibility to each Statistical Area level 2 in New South Wales and the Australian Capital Territory. This index informs a residential population model to identify the relationship between population and accessibility, highlighting the wider impact of transport improvements on the regional economy. The model links population changes to travel demand and predicts induced demand under a business-as-usual scenario. The application of the model system is illustrated using a corridor between Sydney and Newcastle
System capacity model and algorithm for urban multimodal transport network with transfer
No full textThis paper proposes a method for calculating transport networks capacity in dealing with multimodal transfers. Multimodal networks are represented by a modified ‘supernetwork’, while the passenger’s travels are defined as ‘superpaths’. Within this framework, the relation between the travel demand from O-D matrices and the resulting link flows in the supernetwork is modelled as a relationship matrix to describe urban mobility by using a logit-based stochastic user equilibrium. Based on this relationship matrix, an approximate iteration algorithm (AIA) is developed. Our numerical results show that the AIA performs better than the sensitivity analysis-based algorithm (SAB) and genetic algorithm (GA) regarding the execution-time, and that the capacity of multimodal transport networks can be underestimated if the combined travels are neglected
Bimodal transit design with heterogeneous demand elasticity under different fare structures
No full textThe study develops a new optimisation model to design a bimodal transit system from a microeconomic view to maximise the profit of a transit agency considering heterogeneous demand elasticity and different fare structures. Bimodal transit network parameters are optimized to better serve passenger demand. An elastic demand function is devised to include various time components and incorporate flat, distance-based, and hybrid fares. A nested iterative procedure is developed to find a near-optimal solution. Numerical experiments reveal the following interesting findings. First, the increase in elasticity parameters has a knock-on effect on the financial performance, consequently leading to a net profit reduction. Second, a distance-based fare scheme brings in the least actual demand but makes the most profit, compared with the flat and hybrid fare schemes. Third, passengers prefer using a rail-bus system to a BRT-bus system, especially at a higher demand level
Can governments streamline environmental impact analysis to promote transit-oriented development? Evidence from California
No full textCalifornia’s seminal Sustainable Communities and Climate Protection Act of 2008—Senate Bill (SB) 375—includes two provisions specifically intended to help streamline transit-oriented development (TOD) projects through environmental review (California SB 375, 2008). One provision exempts qualifying TODs from environmental review entirely. The other provision streamlines environmental review for qualifying projects. This study explores the use and effect of those provisions. We first quantify how much and where the provisions have been used. We then use interviews and email communications with planning and development practitioners to explore why streamlining is used, whether streamlining actually helps reduce the time, cost, and uncertainty of permitting TOD projects, and how streamlining could be improved to better facilitate TOD projects. We find that SB 375 streamlining is a mixed bag. Neither streamlining provision has been used extensively. The full exemption appears to have been avoided because its costs and complications outweigh any streamlining benefit, though the more limited streamlining provision was regarded as having at least some utility. We also found that SB 375-streamlined projects might not be fulfilling SB 375’s more fundament goals—reducing vehicle kilometers traveled and greenhouse gas emissions. The clearest lesson for policymakers is to reduce the eligibility requirements for environmental review streamlining provisions
Optimal charging scheduling of an electric bus fleet with photovoltaic-storage-charging stations
No full textReplacing conventional diesel buses with widely acclaimed electric buses (EBs) for urban transit services can significantly reduce the operational costs and carbon emissions. However, if a bus fleet relies solely on the electricity grid as its energy supply, existing economic and environmental problems may not be fully overcome due to the grid’s overdependence on non-renewable energy sources such as fossil fuels. This study models and optimizes an emerging bus charging scenario where photovoltaic-storage-charging (PSC) stations and an electricity grid jointly supply electricity to an EB fleet. Each PSC station is equipped with photovoltaic (PV) panels to absorb solar power and a battery set to store electricity, which can either charge buses, supply electricity to the grid, or do both simultaneously when needed. Unlike previous studies, this research not only addresses when, where, and how much electricity each EB in the fleet should be charged but also determines the optimal internal allocation scheme of electricity within each PSC station that minimizes the total charging cost of the EB fleet in its daily operations. It introduces a mixed integer programming problem with time discretization across a time-expanded network. The charging cost of the fleet is calculated in terms of the sum of PV generation cost and time-of-use (TOU) electricity tariff minus the revenue of supplying electricity to the grid. To solve this problem, a Lagrangian relaxation procedure is designed, in which a dynamic programming algorithm implemented as a bi-criterion labeling procedure is developed for the decomposed single-bus charging scheduling subproblem. We collected relevant weather and operational data of an EB fleet operating in Jiading, Shanghai, to validate the model and algorithm and to gain managerial insights. A sensitivity analysis was conducted to examine how key model parameters such as charging demand and supply, PSC battery capacity, and electricity discharging price influence the charging schedule of the EB fleet. Finally, we compared our algorithm’s performance with a state-of-the-practice commercial solver, demonstrating that our algorithm achieves comparable solution optimality while significantly saving computing time
City bus electrification in South Korea: Public preference identified through contingent valuation experiment
No full textTo respond actively to issues of climate change and particulate matters, the South Korean authorities have established a plan to electrify all city buses by 2030. This article explores public preferences for city bus electrification in South Korea through the application of contingent valuation (CV). A CV questionnaire was presented to 1000 randomly chosen households along with visual aids to induce their willingness to pay (WTP) for city bus electrification. The payment vehicle and the survey method were selected as bimonthly household income tax and person-to-person interviewing, respectively. As a method of eliciting the WTP, the one-and-one-half-bound dichotomous choice technique was utilized. To analyze the WTP data with numerous zeros, the spike model was employed. The results revealed statistically significant findings. The mean household WTP was obtained as KRW 5195 (USD 4.0) every two months. Expanding this figure to encompass the entire national population results in an annual national total of KRW 680.6 billion (USD 524.7 million). The cost involved in the electrification amounts to KRW 625.0 billion (USD 481.9 million) every year. Therefore, the national WTP outweighs the cost. City bus electrification can be socially justifiable. Furthermore, some policy issues regarding the success of city bus electrification are discussed
Iterative DEA for public transport transfer efficiency in a super-aging society
No full textWith the advent of a super-aging society, increasing attention is being paid to the mobility needs of elderly public transportation users. This study aims to evaluate the transfer efficiency of public transportation for the elderly by developing a novel decision-making approach, namely the Iterative Data Envelopment Analysis (iDEA) model. The relative efficiency of elderly users compared to general users was estimated for each public transportation station. Subsequently, explainable artificial intelligence (XAI) models were used to identify strategies for improving service quality for elderly users to match that of general users. The results showed that, on average, the efficiency score across 32 transfer stations was 0.89, indicating that both transfer time and its standard deviation would need to be reduced by 11 % to achieve full efficiency. Such improvements would ensure the same level of convenience for elderly users as for general users. The relationship between efficiency scores and transfer walking facilities was also explored using XAI models. The findings suggest that installing a moderate number of escalators and elevators enhances transfer convenience, while excessive installations may cause spatial complexity. These insights offer valuable guidance for long-term transportation planning to accommodate the needs of an aging population
Market segmentation and willingness to pay for public transport annual passes among older adults: insights from Genoa, Italy
No full textA survey of older adults aged 65 and over (n = 247) was conducted in Genoa, often described as ‘the oldest city in the oldest country’ in Europe. This paper presents two scenarios exploring older people\u27s willingness to pay (WTP) for annual tickets for local public transport (LPT) and examines the impact of factors such as cost, time, and comfort. These insights could help address the challenges of travel in ageing societies. A segmentation analysis based on the mean values of the two WTP scenarios (status quo and improved services) was conducted, followed by linear regression modelling to understand how older adults\u27 socio-demographic traits, perceptions, and travel behaviour affect their WTP.
Our findings suggest that the pricing of the annual pass (€345 at the time of survey) exceeds the WTP indicated by respondents. The mean WTP for the status quo level of service (€221.36) was much lower than the WTP if service levels were improved (€304.07). Women were found to be more likely to use LPT but also tended to live alone and have lower incomes. Off-peak hours, particularly in the afternoon, were also associated with a higher WTP. This research is important in the context of Europe\u27s ageing population, highlighting the need for more inclusive transport options for older adults. Public transport authorities (PTAs) should explore more tailored approaches to pricing and service provision. It is imperative to balance the competing goals of cost recovery, equity, and service attractiveness to encourage older people\u27s uptake of LPT services, while maintaining accessibility and wellbeing
The effects of TOD on economic vitality in the post-COVID-19 era
No full textWith the changes in people\u27s spatial cognitions, preferences and behavior patterns as a response to the COVID-19 pandemic crisis, the way transit-oriented development (TOD) boosts economic vitality has enormously altered. Hence, this study employs machine learning methods to explore the effects of TOD on economic vitality under COVID-19 and recalibrate existing TOD planning models and design principles. Based on multi-source data of Hong Kong, it measures economic vitality of MTR station areas with life service reviews and depicts built environment therein from three dimensions including node, tie, and place. It discovers that (1) the outbreak of COVID-19 impaired the economic vitality effects of TOD; (2) the global relative importance of MTR station centrality and ground space index declined during the outbreak and bounced back afterwards, meanwhile, that of street centrality, street betweenness, street detour ratio, and green space coverage increased and that of bus density, MTR station betweenness, and average building height decreased; (3) the economic vitality effects of TOD were nonlinear, and the threshold values and effective ranges of built environment variables remained constant across the time; (4) the economic vitality effects of TOD were moderated by the pandemic. This study enlightens urban policymakers and practitioners with nuanced criteria for pandemic-adaptive TOD planning and design strategies
A human factors-based modeling framework to mimic bus driver behavior
No full textOver the past 50–60 years, numerous driver behavior models have been proposed in the literature. However, the literature still lacks models describing bus drivers’ behavior in traffic streams, even though buses comprise a non-negligible component of the traffic mix in many cities. Further, bus driver behavior might differ from other vehicles due to the differences in size, kinematic characteristics, maneuvering capabilities, and the number of occupants. Moreover, human factors such as multi-vehicle anticipation and stimuli perception contribute to this difference in driver behavior. Motivated by these reasons, this study presents a new modeling framework for mimicking bus driver behavior. The framework incorporates two important aspects of bus driver behavior: multi-vehicle anticipation and stimuli perception. Based on the proposed modeling framework, the study modifies the widely used Intelligent Driver Model (IDM). A variance-based sensitivity analysis is carried out to recognize the influence of model parameters (specifically, the new parameters) on the output of the IDM model. The modified IDM model is calibrated and validated using an empirical trajectory dataset of about 90 buses from a traffic stream in Chennai, India. In doing so, the study also contributes to modelling driver behavior in heterogeneous and disorderly traffic streams found in Indian cities and elsewhere. The parameter calibration results show that the average calibrated parameters of the modified IDM offer realistic interpretations, and the calibration and validation errors are small. Furthermore, it is evident from the results that the perceived space gaps by bus drivers can be longer or shorter than the actual space gaps. Overall, the modified IDM model outperformed the original IDM, highlighting the efficacy of the proposed multi-vehicle anticipation and stimuli perception features in the model. Finally, the study also evaluates the model performance by analysing its stability and macroscopic properties