Publications
212 results found
Singh R, Graham DJ, Trompet M, et al., 2024, A sampling scheme for quantifying and benchmarking on time performance of urban bus transit, Transportation Research Part A: Policy and Practice, Vol: 180, ISSN: 0965-8564
In this paper we use large-scale mass transit data to improve the journey time performancemeasurement of urban bus systems. For low frequency bus services, the application of ontime performance (OTP) metrics, particularly the location and number of stops to sample,varies greatly across operators, which can lead to biased estimates. In this paper, we aim toaddress sampling disparity, and propose a new statistically robust sampling scheme to ensurerepresentative, non-biased, and comparable measurement of route level on time performance(OTP) which enables the inclusion of operators with sparse data in benchmarking activities.We use automated vehicle location data and analyse 59 unique low frequency route-directiondata sets from 9 international bus operators. Across all route-direction data sets, an averageof 28% of stops are required to be sampled under the defined sampling scheme to achieve a95% confidence level of sampling the route-level mean within a ± 5% range of accuracy. Byincluding data on additional routes, the analysis can be further refined and generalised toenable progressive improvement of inter-operator comparability and benchmarking.
Awad FA, Graham DJ, Singh R, et al., 2023, Predicting urban rail transit safety via artificial neural networks, SAFETY SCIENCE, Vol: 167, ISSN: 0925-7535
Xuto P, Bansal P, Anderson RJ, et al., 2023, Examining the impacts of capital investment in London's Underground: A long-term analysis, TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, Vol: 175, ISSN: 0965-8564
Ma L, Graham DJ, Stettler MEJ, 2023, Using Explainable Machine Learning to Interpret the Effects of Policies on Air Pollution: COVID-19 Lockdown in London, ENVIRONMENTAL SCIENCE & TECHNOLOGY, ISSN: 0013-936X
Awad FA, Graham DJ, AitBihiOuali L, et al., 2023, Benchmarking the performance of urban rail transit systems: a machine learning application, TRANSPORTMETRICA A-TRANSPORT SCIENCE, ISSN: 2324-9935
Anupriya, Bansal P, Graham DJ, 2023, Congestion in cities: can road capacity expansions provide a solution?, Transportation Research Part A: Policy and Practice, Vol: 174, Pages: 1-29, ISSN: 0965-8564
Road network congestion; a traffic state characterised by slower speeds, longer trip times, and increased vehicular queuing; is a major issue in most urban areas around the globe. Building more roads is a commonly employed policy intervention to reduce congestion. This strategy, however, is controversial because under certain conditions road capacity expansions may induce growth in traffic volumes. A crucial precursor to understanding whether road capacity expansions provide a solution to congestion is to quantify the technology driving congestion in urban road networks. This congestion technology describes the variation in performance of the network, often represented by traffic flow through the road network, over its intensity of use given by the number of vehicles in the network. However, obtaining empirical estimates of congestion technology from data on traffic variables is challenging due to statistical biases that emerge via the complex interactions between traffic flow, traffic controls, and capacity. To adjust for such biases, this paper presents an approach based on causal statistical modelling to quantify the nature and form of congestion technology in road networks in twenty-four cities worldwide. Our results suggest that increasing network capacity is in general not an efficient solution to manage congestion, in the sense that the average travel speed in the network does not increase substantially with an increase in capacity. This result and our congestion technology estimates have important implications for optimal urban transportation strategies.
Acharya PS, AitBihiOuali L, Matthews HS, et al., 2023, The Impact of Periodic Passenger Vehicle Safety Inspection Programs on Roadway Fatalities: Evidence from US States Using Panel Data, JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS, Vol: 149, ISSN: 2473-2907
Luo Y, Graham DJ, McCoy EJ, 2023, Semiparametric Bayesian doubly robust causal estimation, Journal of Statistical Planning and Inference, Vol: 225, Pages: 171-187, ISSN: 0378-3758
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.
Gong Z, Zhang F, Liu W, et al., 2023, On the effects of airport capacity expansion under responsive airlines and elastic passenger demand, Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Gong Z, Zhang F, Liu W, et al., 2023, On the effects of airport capacity expansion under responsive airlines and elastic passenger demand, Transportation Research Part B: Methodological: an international journal, Vol: 170, Pages: 48-76, ISSN: 0191-2615
This paper investigates the effect of airport expansion on air traffic and its implications on airport congestion, airline competition and the social welfare, considering various airport administrative regimes (i.e., profit-maximization, social welfare-maximization, and budget-constrained social welfare-maximization), airline market structures (i.e., perfectly competitive, oligopoly, monopoly, leader–follower), and passenger demand patterns (i.e., regard airlines as perfect substitutes or imperfect substitutes). We develop an analytical tri-level model to examine the air traffic equilibrium in the airport–airline–passenger system, the effect of airport capacity expansion on the traffic equilibrium, and the decisions of different stakeholders. Specifically, we examine the airport’s decisions on capacity investment and airport charge in the first level, airlines’ decisions on flight volume and airfare in the second level, and the passenger choice equilibrium in the third level. The analysis in this paper suggests that (i) airport capacity expansion may induce the airline market to over schedule flights which leads to a more congested airport (i.e., capacity paradox); (ii) with a given airport charge, the capacity paradox is more likely to occur in an airline market with fewer competitive airlines; (iii) given the same airport capacity and traffic, the capacity paradox is more likely to occur when the airport operator’s objective is social welfare-maximization (compared with profit-maximization and budget-constrained social welfare-maximization); (iv) airlines with market power will internalize a portion of airport congestion based on their market share, while a leader airline with the knowledge of the follower’s response will scale up or down their airfare in order to maximize its profit; (v) under different market structures, increasing airport charge always reduces the aggregate traffic volume when the airport capacity is fi
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
Awad FAA, Graham DJJ, AitBihiOuali L, et al., 2023, Performance of urban rail transit: a review of measures and interdependencies, Transport Reviews, Vol: 43, Pages: 698-725, ISSN: 0144-1647
Recent years saw immense growth in performance measurement literature related to public transit systems, with a clear segmentation between financial and quality-of-service performance frameworks. Recently, there has been a shift away from considering cost efficiency alone as a performance measure, and quality-of-service – which influences ridership attraction and retention – has been receiving more interest. The segmentation of these two performance aspects poses a gap in the literature, as there are interdependencies between them. This study provides a systematic review of the methodologies and empirical findings of studies on both performance measurement aspects of urban rail transit systems; specifically, we demonstrate the importance of linking cost efficiency analyses to the level of service quality. To our knowledge, this is the first review of urban rail transit research that links the two performance aspects. We begin by reviewing the methodological limitations of cost performance measures and summarising the drivers of cost performance in the existing literature. We then review studies on the definitions and measurements of quality-of-service in urban rail performance. Lastly, we summarise the scant literature linking the two performance aspects and highlight future study directions, mainly, the importance of a structural framework to provide a holistic view of transit operators’ performance.
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
Anupriya, Bansal P, Graham DJ, 2022, Modelling the propagation of infectious disease via transportation networks, Scientific Reports, Vol: 12, ISSN: 2045-2322
The dynamics of human mobility have been known to play a critical role in the spread of infectious diseases like COVID-19. In this paper, we present a simple compact way to model the transmission of infectious disease through transportation networks using widely available aggregate mobility data in the form of a zone-level origin-destination (OD) travel flow matrix. A key feature of our model is that it not only captures the propagation of infection via direct connections between zones (first-order effects) as in most existing studies but also transmission effects that are due to subsequent interactions in the remainder of the system (higher-order effects). We demonstrate the importance of capturing higher-order effects in a simulation study. We then apply our model to study the first wave of COVID-19 infections in (i) Italy, and, (ii) the New York Tri-State area. We use daily data on mobility between Italian provinces (province-level OD data) and between Tri-State Area counties (county-level OD data), and daily reported caseloads at the same geographical levels. Our empirical results indicate substantial predictive power, particularly during the early stages of the outbreak. Our model forecasts at least 85% of the spatial variation in observed weekly COVID-19 cases. Most importantly, our model delivers crucial metrics to identify target areas for intervention.
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.
Graham DJ, Singh R, 2022, Model-based adjustment for conditional benchmarking, IMA Journal of Management Mathematics, Vol: 33, Pages: 381-393, ISSN: 1471-678X
Quantitative benchmarking is widely used in the industry to compare relative performance across a sample of organizations. A key analytical challenge lies in obtaining accurate measures of intrinsic organizational performance net of contextual or exogenous influences. In this paper, we propose a model-based adjustment approach for comparative benchmarking that allows the analyst to recover targeted metrics for specific aspects of innate performance. We outline the statistical theory underpinning our method, provide simulations to demonstrate its properties and describe practical examples for computation. The managerial relevance of the method is demonstrated via two real-world transport industry applications: adjusting for economies of scale and density in benchmarking average costs of urban metros and for service characteristics in benchmarking metro journey times.
Bansal P, Kessels R, Krueger R, et al., 2022, Preferences for using the London Underground during the COVID-19 pandemic, TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, Vol: 160, Pages: 45-60, ISSN: 0965-8564
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- Citations: 7
Anupriya, Bansal P, Graham DJ, 2022, Analytical Representations of the Fundamental Diagram of Traffic Flow for Highways: A Review of Theory and Empirics
Singh R, Sood R, Graham D, 2022, Road traffic casualties in Great Britain at daylight savings time transitions: a causal regression discontinuity design analysis, BMJ Open, Vol: 12, ISSN: 2044-6055
Objectives: To determine whether daylight savings time (DST) transitions have an effect on road traffic casualties in Great Britain using causal regression discontinuity design analysis. We undertake aggregate and disaggregate spatial and temporal analyses to test the commonly referenced sleep and light hypotheses.Design: The study takes the form of a natural experiment in which the DST transitions are interventions to be evaluated. Two outcomes are tested: (i) the total number of casualties of all severities (ii) the number of fatalities.Data: Data are obtained from the UK Department for Transport STATS19 database. Over a period of 14 years between 2005 and 2018, 311,766 total casualties and 5,429 fatalities occurred 3 weeks either side of the Spring DST transition and 367,291 total casualties and 6,650 fatalities occurred 3 weeks either side of the Autumn DST transition. Primary outcome measure: A regression discontinuity design method (RDD) is applied. The presence of a causal effect is determined via the degree of statistical significance and magnitude of the average treatment effect.Results: All significant average treatment effects are negative (54 significant models out of 287 estimated), indicating that there are fewer casualties following the transitions. Overall, bootstrapped summary statistics indicate a reduction of 0.75 in the number of fatalities (95% CI: -1.61, -0.04) and a reduction of 4.73 in the number of total casualties (95% CI: -6.08, -3.27) on average per year at both the Spring and Autumn DST transitions combined.Conclusions: The results indicate minor reductions in the number of fatalities following the DST transitions, and thus our analysis does not support the most recent UK parliamentary estimate that there would be 30 fewer fatalities in Great Britain if DST were to be abolished. Furthermore, the results do not provide conclusive support for either the sleep or light hypotheses.
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
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- Citations: 32
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
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- Citations: 1
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.
Bhuyan P, McCoy EJ, Li H, et al., 2021, Analysing the causal effect of London cycle superhighways on traffic congestion, Annals of Applied Statistics, Vol: 15, Pages: 1999-2022, ISSN: 1932-6157
Transport operators have a range of intervention options available to improve or enhance their networks. Such interventions are often made in the absence of sound evidence on resulting outcomes. Cycling superhighways were promoted as a sustainable and healthy travel mode, one of the aims of which was to reduce traffic congestion. Estimating the impacts that cycle superhighways have on congestion is complicated due to the nonrandom assignment of such intervention over the transport network. In this paper we analyse the causal effect of cycle superhighways utilising preintervention and postintervention information on traffic and road characteristics along with socioeconomic factors. We propose a modeling framework based on the propensity score and outcome regression model. The method is also extended to the doubly robust set-up. Simulation results show the superiority of the performance of the proposed method over existing competitors. The method is applied to analyse a real dataset on the London transport network. The methodology proposed can assist in effective decision making to improve network performance.
Ma L, Graham D, Stettler M, 2021, Has the Ultra Low Emission Zone in London improved air quality?, Environmental Research Letters, Vol: 16, Pages: 1-16, ISSN: 1748-9326
London introduced the world's most stringent emissions zone, the Ultra Low Emission Zone (ULEZ), in April 2019 to reduce air pollutant emissions from road transport and accelerate compliance with the EU air quality standards. Combining meteorological normalisation, change point detection, and a regression discontinuity design with time as the forcing variable, we provide an ex-post causal analysis of air quality improvements attributable to the London ULEZ. We observe that the ULEZ caused only small improvements in air quality in the context of a longer-term downward trend in London's air pollution levels. Structural changes in nitrogen dioxide (NO2) and ozone (O3) concentrations were detected at 70% and 24% of the (roadside and background) monitoring sites and amongst the sites that showed a response, the relative changes in air pollution ranged from −9% to 6% for NO2, −5% to 4% for O3, and −6% to 4% for particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5). Aggregating the responses across London, we find an average reduction of less than 3% for NO2 concentrations, and insignificant effects on O3 and PM2.5 concentrations. As other cities consider implementing similar schemes, this study implies that the ULEZ on its own is not an effective strategy in the sense that the marginal causal effects were small. On the other hand, the ULEZ is one of many policies implemented to tackle air pollution in London, and in combination these have led to improvements in air quality that are clearly observable. Thus, reducing air pollution requires a multi-faceted set of policies that aim to reduce emissions across sectors with coordination among local, regional and national government.
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
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