30 results found
Hörcher D, De Borger B, Graham DJ, 2023, Subsidised transport services in a fiscal federation: Why local governments may be against decentralised service provision, Economics of Transportation, Vol: 34, Pages: 1-18, ISSN: 2212-0122
In this paper we consider a fiscal federation and study the effects of decentralised provision of loss-generating public services with benefit spillovers to other regions. We use public transport provision across administrative borders as a prototype example. We show in a formal model that local governments might be better off when a higher-level government or a neighbouring region provides these services, and even privatisation to a monopolist can be preferred over decentralisation. Our model reveals that these results are governed by a variant of the tax exporting mechanism that applies to subsidised services, i.e., the possibility that local consumers can exploit spillover benefits without contributing to the subsidy burden of service provision. Public transport provision is one of the large sectors of public policy where decentralisation could provide social benefits, but, as the paper reveals, the need for subsidies generates a genuine conflict of interest between the governments involved.
Singh R, Horcher D, Graham DJ, 2023, An evaluation framework for operational interventions on urban mass public transport during a pandemic, SCIENTIFIC REPORTS, Vol: 13, ISSN: 2045-2322
Zimmo I, Hörcher D, Singh R, et al., 2023, Benchmarking Travel Time and Demand Prediction Methods Using Large-scale Metro Smart Card Data, Periodica Polytechnica Transportation Engineering, Vol: 51, Pages: 357-374, ISSN: 0303-7800
Urban mass transit systems generate large volumes of data via automated systems established for ticketing, signalling, and other operational processes. This study is motivated by the observation that despite the availability of sophisticated quantitative methods, most public transport operators are constrained in exploiting the information their datasets contain. This paper intends to address this gap in the context of real-time demand and travel time prediction with smart card data. We comparatively benchmark the predictive performance of four quantitative prediction methods: multivariate linear regression (MVLR) and semiparametric regression (SPR) widely used in the econometric literature, and random forest regression (RFR) and support vector machine regression (SVMR) from machine learning. We find that the SVMR and RFR methods are the most accurate in travel flow and travel time prediction, respectively. However, we also find that the SPR technique offers lower computation time at the expense of minor inefficiency in predictive power in comparison with the two machine learning methods.
Xuto P, Anderson RJ, Graham DJ, et al., 2023, Sustainable urban rail funding: Insights from a century-long global dataset, TRANSPORT POLICY, Vol: 130, Pages: 100-115, ISSN: 0967-070X
Zhang N, Graham DJ, Bansal P, et al., 2022, Detecting metro service disruptions via large-scale vehicle location data, Transportation Research Part C: Emerging Technologies, Vol: 144, Pages: 1-19, ISSN: 0968-090X
Urban metro systems are often affected by disruptions such as infrastructure malfunctions, rolling stock breakdowns and accidents. The crucial prerequisite of any disruption analytics is to have accurate information about the location, occurrence time, duration and propagation of disruptions. To pursue this goal, we detect the abnormal deviations in trains’ headway relative to their regular services by using Gaussian mixture models. Our method is a unique contribution in the sense that it proposes a novel, probabilistic, unsupervised clustering framework and it can effectively detect any type of service interruptions, including minor delays of just a few minutes. In contrast to traditional manual inspections and other detection methods based on social media data or smart card data, which suffer from human errors, limited monitoring coverage, and potential bias, our approach uses information on train trajectories derived from automated vehicle location (train movement) data. As an important research output, this paper delivers innovative analyses of the propagation progress of disruptions along metro lines, which enables us to distinguish primary and secondary disruptions as well as effective recovery interventions performed by operators.
Horcher D, Singh R, Graham D, 2022, Social distancing in public transport: mobilising new technologies for demand management under the Covid-19 crisis, Transportation, Vol: 49, Pages: 735-764, ISSN: 0049-4488
Dense urban areas are especially hardly hit by the Covid-19 crisis due to the limited availability of public transport, one of the most efficient means of mass mobility. In light of the Covid-19 pandemic, public transport operators are experiencing steep declines in demand and fare revenues due to the perceived risk of infection within vehicles and other facilities. The purpose of this paper is to explore the possibilities of implementing social distancing in public transport in line with epidemiological advice. Social distancing requires effective demand management to keep vehicle occupancy rates under a predefined threshold, both spatially and temporally. We review the literature of five demand management methods enabled by new information and ticketing technologies: (i) inflow control with queueing, (ii) time and space dependent pricing, (iii) capacity reservation with advance booking, (iv) slot auctioning, and (v) tradeable travel permit schemes. Thus the paper collects the relevant literature into a single point of reference, and provides interpretation from the viewpoint of practical applicability during and after the pandemic.
Horcher D, Singh R, Graham DJ, 2022, Social distancing in public transport: mobilising new technologies for demand management under the Covid-19 crisis, Publisher: SPRINGER
Bansal P, Horcher D, Graham D, 2022, A dynamic choice model to estimate the user cost of crowding with large scale transit data, Journal of the Royal Statistical Society Series A: Statistics in Society, Vol: 185, ISSN: 0964-1998
Efficient mass transit provision should be responsive to the behaviour of passengers. Operators often conduct surveys to elicit passenger perspectives, but these can be expensive to administer and can suffer from hypothetical biases. With the advent of smart card and automated vehicle location data, operators have reliable sources of revealed preference (RP) data that can be utilized to estimate transit riders’ valuation of service attributes. To date, effective use of RP data has been limited due tomodelling complexities. We propose a dynamic choice model (DCM) for population-level longitudinal RP data to address prominent challenges. In the DCM, riders are assumed to follow different decision rules (compensatory and inertia/habit) and temporal switching between decision rules based onexperience-based learning is also formulated. We develop an expectation-maximization algorithm to estimate the DCM and apply our model to estimate passenger valuation of crowding. Using large-scale data of two months with over four million daily trips by an Asian metro, our DCM estimates show an increase of 47% in passenger’s valuation of travel time under extremely crowded conditions. Furthermore, the average passenger follows the compensatory rule on only 25.5% or fewer trips. These results are valuable for supply-side decisions of transit operators.
Hoercher D, Graham DJ, 2022, Multimodal Substitutes in Public Transport Efficient Variety or Wasteful Competition?, Publisher: UNIV BATH
Zhang N, Graham DJ, Hörcher D, et al., 2021, A causal inference approach to measure the vulnerability of urban metro systems, Transportation, Vol: 48, Pages: 3269-3300, ISSN: 0049-4488
Transit operators need vulnerability measures to understand the level of service degradation under disruptions. This paper contributes to the literature with a novel causal inference approach for estimating station-level vulnerability in metro systems. The empirical analysis is based on large-scale data on historical incidents and population-level passenger demand. This analysis thus obviates the need for assumptions made by previous studies on human behaviour and disruption scenarios. We develop four empirical vulnerability metrics based on the causal impact of disruptions on travel demand, average travel speed and passenger flow distribution. Specifically, the proposed metrics based on the irregularity in passenger flow distribution extends the scope of vulnerability measurement to the entire trip distribution, instead of just analysing the disruption impact on the entry or exit demand (that is, moments of the trip distribution). The unbiased estimates of disruption impact are obtained by adopting a propensity score matching method, which adjusts for the confounding biases caused by non-random occurrence of disruptions. An application of the proposed framework to the London Underground indicates that the vulnerability of a metro station depends on the location, topology, and other characteristics. We find that, in 2013, central London stations are more vulnerable in terms of travel demand loss. However, the loss of average travel speed and irregularity in relative passenger flows reveal that passengers from outer London stations suffer from longer individual delays due to lack of alternative routes.
Horcher D, Graham DJ, 2021, Multimodal substitutes in public transport: Efficient variety or wasteful competition?, Journal of Transport Economics and Policy, ISSN: 0022-5258
The question we raise is whether it is desirable under public ownership to run multiplepublic transport services, e.g., buses and trains, along a transport corridor, when thesemodes are (imperfect) substitutes. The paper applies the theory of product differentiationin the context of social welfare oriented public transport provision. We react to ongoingpolicy debates by showing that modal variety may well be beneficial for society, if thespread of consumer preferences is sufficiently wide and the magnitude of scale economiesin service provision is limited. This point is supported by theory and illustrated with anagent-based simulation model.
Singh R, Graham D, Horcher D, et al., 2021, The boundary between random and non-random passenger arrivals: robust empirical evidence and economic implications, Transportation Research Part C: Emerging Technologies, Vol: 130, ISSN: 0968-090X
In this paper, we investigate the influence of train headways on passenger platform wait times using automated data from the London Underground metro system. For high frequency services, the literature suggests that passenger arrivals are random and that under perfectly random conditions with all other factors held constant, wait times are equivalent to half of the headway between trains. We test this hypothesis using large-scale smart card and vehicle location data, which enables the extraction of access times from total passenger journey times as well as the precise measurement of train headways. Using a semi para-metric regression modelling framework, we generate non-linear estimates of the relationship between access times and headway while conditioning for other service supply and demand factors. Marginal platform wait times are then derived numerically via an exposure-response model framework which accounts for potential confounding between the walking and waiting components of access times, thus enabling quantification of the unbiased impact of headways on wait times. For three lines in central London, we observe that marginal wait times transition from greater than half of the headway to approximately one third of the headway astrain frequencies decrease. The transition occurs in the range between 2-3 minute headways, lower than earlier estimates in the literature. A series of numerical simulations illustrate the importance of waiting time sensitivity in the optimisation of public transport services. In comparison with the standard wait time assumption, our exercise reveals that the degree of density economies is milder than what the literature suggests, and this may neutralise some of the economic justifications of high public transport subsidies
Xuto P, Anderson R, Graham D, et al., 2021, Optimal infrastructure reinvestment in urban rail systems: A dynamic supply optimisation approach, Transportation Research Part A: Policy and Practice, Vol: 147, Pages: 251-268, ISSN: 0191-2607
The state of infrastructure in many developed countries around the world is an increasingly pressing issue, with mounting costs the longer repairs are deferred. In today’s rapidly urbanising world, the urban rail network is particularly critical, since infrastructure failures can have severe economic consequences for both the operator’s finances and user time costs. This paper thus provides a system-level model of welfare-oriented supply optimisation that integrates asset management with the literature on optimal pricing and capacity provision. Using a simulation approach and calibrating with London Underground data, this paper delivers three key contributions. First, the economic efficiency of long-term capital planning is highlighted, with up to an 87% welfare gain when comparing a 40- versus 5-year planning horizon. Second, in general, the longer the planning horizon, the higher the annual welfare, demand, asset condition, fare and supply, in the steady-state. Third, the analysis explores why policies in reality diverge from the welfare optimum: we show that election cycles can have a detrimental effect, with increased asset neglect and volatility in spending. Economic efficiency improves in the short-term at the expense of the long-term; significant intervention is needed to break this downwards trend, as also reflected in various rail systems’ histories.
Hörcher D, Tirachini A, 2021, A review of public transport economics, Economics of Transportation, Vol: 25, Pages: 1-34, ISSN: 2212-0122
Public transport provision requires substantial organisational efforts, careful planning, financial contributions from the public, and coordination between millions of passengers and staff members in large systems. Efficient resource allocation is critical in its daily operations. Therefore, public transport has been among the most popular subjects in transport economics since the infancy of this discipline. This paper presents an overview of the literature developed over the past half century, including more than 300 important contributions. With a strong methodological orientation, it collects, classifies, and compares the frequently used analytical modelling techniques, thus providing a cookbook for future research and learning efforts. We discuss key findings on optimal capacity provision, pricing, cost recovery and subsidies, externalities, private operations, public service regulation, and cross-cutting subjects, such as interlinks with urban economics, political economy, and emerging mobility technologies.
Anupriya, Graham DJ, Bansal P, et al., 2020, Congestion in near capacity metro operations: optimum boardings and alightings at bottleneck stations
During peak hours, metro systems often operate at high service frequencies totransport large volumes of passengers. However, the punctuality of suchoperations can be severely impacted by a vicious circle of passenger congestionand train delays. In particular, high volumes of passenger boardings andalightings may lead to increased dwell times at stations, that may eventuallycause queuing of trains in upstream. Such stations act as active bottlenecks inthe metro network and congestion may propagate from these bottlenecks to theentire network. Thus, understanding the mechanism that drives passengercongestion at these bottleneck stations is crucial to develop informed controlstrategies, such as control of inflow of passengers entering these stations. Tothis end, we conduct the first station-level econometric analysis to estimate acausal relationship between boarding-alighting movements and train flow usingdata from entry/exit gates and train movement data of the Mass Transit Railway,Hong Kong. We adopt a Bayesian non-parametric spline-based regression approachand apply instrumental variables estimation to control for confounding biasthat may occur due to unobserved characteristics of metro operations. Throughthe results of the empirical study, we identify bottleneck stations and provideestimates of optimum passenger movements per train and service frequencies atthe bottleneck stations. These estimates, along with real data on daily demand,could assist metro operators in devising station-level control strategies.
Anupriya A, Graham DJ, Horcher D, et al., 2020, Quantifying the ex-post causal impact of differential pricing on commuter trip scheduling in Hong Kong, Transportation Research Part A: Policy and Practice, Vol: 141, Pages: 16-34, ISSN: 0191-2607
This paper quantifies the causal impact of differential pricing on the trip-scheduling of regular commuters using the Mass Transit Railway (MTR) in Hong Kong. It does so by applying a difference-in-difference (DID) method to large scale smart card data before and after the introduction of the Early Bird Discount (EBD) pricing intervention. We find statistically significant but small effects of the EBD in the form of earlier departure times. Leveraging the granularity of the data, we also allow for the treatment effect to vary over observed travel characteristics. Our empirical results suggest that fares and crowding are the key determinants of commuter responsiveness to the EBD policy.
Horcher D, Graham D, 2020, The Gini index of demand imbalances in public transport, Transportation, Vol: 48, Pages: 2521-2544, ISSN: 0049-4488
The paper studies a general bidirectional public transport line along which demand varies by line section. The length of line sections also varies, and therefore their contribution to aggregate (line-level) user and operational costs might be different, even if demand levels were uniform. The paper proposes the Gini index as a measure of demand imbalances in public transport. We run a series of numerical simulations with randomised demand patterns, and derive the socially optimal fare, frequency and vehicle size variables in each case. We show that the Gini coefficient is a surprisingly good predictor of all three attributes of optimal supply. These results remain robust with inelastic as well as elastic demand, at various levels of aggregate demand intensity. In addition, we find that lines facing severe demand imbalances generate higher operational cost and require more public subsidies under socially optimal supply, controlling for the scale of operations. The results shed light on the bias introduced by the assumption of homogeneous demand in several existing public transport models.
Lu Q, Tettamanti T, Hörcher D, et al., 2020, The impact of autonomous vehicles on urban traffic network capacity: an experimental analysis by microscopic traffic simulation, Transportation Letters, Vol: 12, Pages: 540-549, ISSN: 1942-7867
Horcher D, Graham DJ, 2020, MaaS economics: Should we fight car ownership with subscriptions to alternative modes?, Economics of Transportation, Vol: 22, ISSN: 2212-0122
Proponents of the Mobility as a Service concept claim that subscriptions to alternative modes can effectively reduce car ownership and the adverse effects of underpriced car use. We test this hypothesis in a microeconomic model with endogenous mode choice as well as car and subscription ownership. The model contains congestible urban rail and car sharing options as substitutes of underpriced private car use. We find that aggregate car ownership is not a reliable proxy for road congestion: subscriptions may reduce car ownership while increasing the vehicle miles travelled by remaining car owners. Subscriptions induce welfare losses for two reasons. First, pass holders overconsume the alternative modes, as the marginal fare they face drops to zero. Second, non-pass holders tend to shift to car use due to the crowding induced by pass holders, causing additional distortions. We illustrate numerically that differentiated pricing is more efficient in achieving the goals of MaaS.
Singh R, Graham DJ, Horcher D, et al., 2020, Decomposing journey time variance on urban metro systems via semiparametric mixed methods, Transportation Research Part C: Emerging Technologies, Vol: 114, Pages: 140-163, ISSN: 0968-090X
The availability of automated data for urban metro systems allows operators to accurately measure journey time reliability. However, there remains limited understanding of the causes of journey time variance and how journey time performance can be improved. In this paper, we present a semiparametric regression modelling framework to determine the underlying drivers of journey time variance in urban metro systems, using the London Underground as a case study. We merge train location and passenger trip data to decompose total journey times into three constituent parts: access times as passengers enter the system, on-train times, and egress times as passengers exit at their destinations. For each journey time component, we estimate non-linear functional relationships which we then use to derive elasticity estimates of journey times with respect to service supply and demand factors, including operational and physical characteristics of metros as well as passenger demand and passenger-specific travel characteristics. We find that the static fixed physical characteristics of stations and routes have the greatest influence on journey time, followed by train speeds, and headways, for which the average elasticities of total journey time are −0.54 and 0.05, respectively. The results of our analysis could inform operators about where potential interventions should be targeted in order to improve journey time performance.
Hörcher D, De Borger B, Seifu W, et al., 2020, Public transport provision under agglomeration economies, Regional Science and Urban Economics, Vol: 81, Pages: 1-19, ISSN: 0166-0462
The purpose of this paper is to investigate, using both theoretical and numerical analysis, the impact of agglomeration externalities on short-run policy decisions in public transport, i.e. socially optimal pricing, frequency setting, and subsidisation. We develop a simple two-mode model in which commuters can opt for car or public transport use; car use leads to congestion, and public transport is subject to crowding. Allowing for agglomeration externalities, we show the following results. First, if car use is correctly priced for congestion, agglomeration benefits imply substantially lower public transport fares and higher frequencies. They neutralise to some extent the pressure to increase fares to correct the crowding externality. Second, as a consequence, agglomeration benefits justify low cost recovery ratios in public transport. Assuming an agglomeration elasticity of 1.04, a value well within the range of reported elasticities, numerical implementation of the model finds that cost-recovery ratios are 35.8% lower than in the absence of the productivity externality. Third, interestingly, the effect of agglomeration benefits on fares and frequency is much smaller if road use is exogenously under-priced. In this case, any modal shift induced by lower public transport fares has opposing agglomeration effects on the two modes, since agglomeration benefits are not mode-specific.
Anupriya A, Graham D, Horcher D, 2019, Existence of Hypercongestion in Highways: A truth or a fallacy?, ITEA Annual Conference on Transportation Economics
Horcher D, Graham DJ, 2018, Demand imbalances and multi-period public transport supply, Transportation Research Part B: Methodological: an international journal, Vol: 108, Pages: 106-126, ISSN: 0191-2615
This paper investigates multi-period public transport supply, i.e. networks in which capacity cannot be differentiated between links and time periods facing independent but nonidentical demand conditions. This setting is particularly relevant in public transport, as earlier findings on multi-period road supply cannot be applied when the user cost function, defined as the sum of waiting time and crowding costs, is nonhomogeneous. The presence of temporal, spatial and directional demand imbalances is unavoidable in a public transport network. It is not obvious, however, how the magnitude of demand imbalances may affect its economic and financial performance. We show in a simple back-haul setting with elastic demand, controlling for total willingness to pay in the network, that asymmetries in market size reduce the attainable social surplus of a service, while variety in maximum willingness to pay leads to higher aggregate social surplus and lower subsidy under efficient pricing. The analysis of multi-period supply sheds light on the relationship between urban structure, daily activity patterns, and public transport performance.
Anupriya A, Graham D, Horcher D, et al., 2018, The impact of early bird scheme on commuter trip scheduling in Hong Kong: a causal analysis using travel card data, Transportation Research Board 97th Annual MeetingTransportation Research Board
Horcher D, Graham DJ, Anderson RJ, 2017, The economics of seat provision in public transport, Transportation Research Part E: Logistics and Transportation Review, Vol: 109, Pages: 277-292, ISSN: 1366-5545
Seated and standing travelling imply significantly different experience for public transport users. This paper investigates with analytical modelling and numerical simulations how the optimal seat supply depends on demand and supply characteristics. The paper explores the implications of seat provision on the marginal cost of travelling as well. In crowded conditions, we distinguish two types of external costs: crowding density and seat occupancy externalities. We derive, using a realistic smart card dataset, the externality pattern of a metro line, and identify the distorting role of the occupancy externality that makes the welfare maximising fare disproportionate to the density of crowding.
Horcher D, Graham DJ, Anderson RJ, 2017, The economic inefficiency of travel passes under crowding externalities and endogenous capacity, Journal of Transport Economics and Policy, ISSN: 0022-5258
Horcher D, Graham DJ, Anderson RJ, 2017, Crowding cost estimation with large scale smart card and vehicle location data, Transportation Research Part B: Methodological: an international journal, Vol: 95, Pages: 105-125, ISSN: 0191-2615
Crowding discomfort is an external cost of public transport trips imposed on fellow passengers that has to be measured in order to derive optimal supply-side decisions. This paper presents a comprehensive method to estimate the user cost of crowding in terms of the equivalent travel time loss, in a revealed preference route choice framework. Using automated demand and train location data we control for fluctuations in crowding conditions on the entire length of a metro journey, including variations in the density of standing passengers and the probability of finding a seat. The estimated standing penalty is 26.5% of the uncrowded value of in-vehicle travel time. An additional passenger per square metre on average adds 11.9% to the travel time multiplier. These results are in line with earlier revealed preference values, and suggest that stated choice methods may overestimate the user cost of crowding. As a side-product, and an important input of the route choice analysis, we derive a novel passenger-to-train assignment method to recover the daily crowding and standing probability pattern in the metro network.
Horcher D, Graham DJ, 2016, Crowding and the marginal cost of travelling under second-best capacity provision, International Transport Economics Association Annual Conference
The classic economic theory of capacity optimisation in public transport suggests that the welfare maximising frequency and vehicle size increase with demand, and therefore the optimal occupancy rate may not dependent on demand; crowding can be internalised through capacity adjustment. On the other hand, empirical studies show that the crowding externality does contribute significantly to the social cost of public transport usage in large metropolitan areas. This paper presents a theoretical framework that explains why rational second-best capacity provision may lead to a wide range of demand dependent crowding levels under economies of vehicle size, infrastructure constraints and demand fluctuations. We derive the marginal external waiting time, crowding and operational costs of travelling for second-best scenarios, and explore the resulting subsidy rates. Thus, we take an important step towards the full understanding of optimal demand and crowding dependent pricing in public transport.
Horcher D, Graham DJ, 2015, The dark side of travel passes: Wrong incentive in crowding, Transportation Research Board 95th Annual Meeting, Washington D.C.
Horcher D, Graham DJ, Anderson R, 2015, The link between crowding pricing and seat supply in public transport, Transportation Research Board 95th Annual Meeting, Washington D.C.
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