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  • Journal article
    Xuto P, Anderson RJ, Graham DJ, Hörcher Det al., 2023,

    Sustainable urban rail funding: Insights from a century-long global dataset

    , Transport Policy, Vol: 130, Pages: 100-115, ISSN: 0967-070X

    In recent years, urban rail funding has become an increasing concern in some western cities. The underlying issues over ridership and funding has largely been driven by trends such as increasing teleworking and new transport modes like ridesharing, which are likely to further exacerbate funding issues in the aftermath of the worldwide health crisis. This paper contributes to the discussion through the examination of funding within urban rail transport, specifically the strengths and weaknesses of mechanisms that can be used to achieve sustainable, stable long-term funding. A unique, very long-term historical dataset of five large metros around the world was collected for this research, with the analysis based on evidence of actual practices, from a relatively rare organisational perspective. Key results include: i) appropriate fare-setting has been critical for long-term financial health, but is vulnerable to inflation effects and political interference; indexing is important but use of consumer prices has led to revenue erosion in real terms, since wage growth is typically higher. ii) How subsidies are generated can have varying impacts on funding stability and sustainability — dedicated taxes and cross-subsidies from road charges are typically better than direct grants as they are secured by legislation; they also reduce political changes and avoid competing claims from other types of government spending (health, education), as compared with grants. iii) Commercial revenue can be a valuable source for future growth, in light of increased resistance to taxation. Real estate-related revenues in particular can be substantial, as in Hong Kong and Tokyo, with historically faster growth than wages, while also capturing any further value increases from rail system improvements.
  • Journal article
    Anupriya, Bansal P, Graham DJ, 2022,

    Modelling the propagation of infectious disease via transportation networks.

    , Sci Rep, Vol: 12

    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.
  • Journal article
    Zhang N, Graham DJ, Bansal P, Hörcher Det 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.
  • Journal article
    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.
  • Journal article
    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.
  • Journal article
    Bansal P, Kessels R, Krueger R, Graham DJet 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
  • Journal article
    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.
  • Journal article
    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.
  • Journal article
    Zhang N, Graham DJ, Hörcher D, Bansal Pet 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.
  • Journal article
    Bhuyan P, McCoy EJ, Li H, Graham DJet 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.
  • Journal article
    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.
  • Journal article
    Horcher D, Graham DJ,

    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.
  • Journal article
    Pogonyi CG, Graham DJ, Carbo JM, 2021,

    Metros, agglomeration and displacement. Evidence from London

    , Regional Science and Urban Economics, Vol: 90, ISSN: 0166-0462

    This paper uses data on the location and movement of establishments and employment in London to estimate the impact of a metro opening on the spatial distribution of economic activity. In addition to fixed effect methods, we employ a planned-route instrumental variables methodology which uses planned but abandoned metro alignments. We find that areas within walking distance to stations experience a positive effect, whereas areas further but still within 2000 meters experience a significant negative impact. Our results provide empirical evidence for the model of Redding and Turner (2015) as areas close to the transport scheme but not subject to it are worse off than areas further away. The results suggest no growth, only displacement on the local level: the metro shifted economic activity closer to the stations.
  • Journal article
    Singh R, Graham D, Horcher D, Anderson Ret 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
  • Journal article
    Buddhavarapu P, Bansal P, Prozzi JA, 2021,

    A new spatial count data model with time-varying parameters

    , TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, Vol: 150, Pages: 566-586, ISSN: 0191-2615
  • Journal article
    Ait Bihi Ouali L, Musuuga D, Graham D, 2021,

    Quantifying responses to changes in the jurisdiction of a congestion charge: a study of the London western extension

    , PLoS One, Vol: 16, ISSN: 1932-6203

    This paper quantifies behavioural responses to changes in the jurisdiction of a congestion charge, with a successive focus on (i) an extension and (ii) a reduction in the size of the charging zone. We exploit the unanticipated nature of both the implementation and removal of London’s Western Expansion Zone (WEZ) as quasi-natural experiments to test whether individual responses to policies are asymmetric. We use the UK Department of Transport Annual Average Daily Flow (AADF) data, which records traffic flows for seven transport modes (including cars, buses, bicycles, heavy and light goods vehicles). Using a difference-in-differences approach, we find that the introduction of the WEZ led to a 4.9% decline in road traffic flows in the new congestion charge area. These results are robust to different model specifications. HGVs traffic did not significantly change post-WEZ, which indicates that their road demand is price inelastic. The removal of the WEZ led to no significant variations in traffic. This result indicates asymmetry in behaviour with persistent changes in post-intervention traffic demand levels.
  • Journal article
    Bansal P, Kumar RR, Raj A, Dubey S, Graham DJet al., 2021,

    Willingness to pay and attitudinal preferences of Indian consumers for electric vehicles

    , ENERGY ECONOMICS, Vol: 100, ISSN: 0140-9883
  • Journal article
    Xuto P, Anderson R, Graham D, Horcher Det 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.
  • Journal article
    Ait Bihi Ouali L, Graham D, 2021,

    The impact of the MeToo scandal on women’s perceptions of security

    , Transportation Research Part A: Policy and Practice, Vol: 147, Pages: 269-269
  • Journal article
    Bansal P, Krueger R, Graham DJ, 2021,

    Fast Bayesian estimation of spatial count data models

    , Computational Statistics & Data Analysis, Vol: 157, Pages: 1-19, ISSN: 0167-9473

    Spatial count data models are used to explain and predict the frequency of phenomena such as traffic accidents in geographically distinct entities such as census tracts or road segments. These models are typically estimated using Bayesian Markov chain Monte Carlo (MCMC) simulation methods, which, however, are computationally expensive and do not scale well to large datasets. Variational Bayes (VB), a method from machine learning, addresses the shortcomings of MCMC by casting Bayesian estimation as an optimisation problem instead of a simulation problem. Considering all these advantages of VB, a VB method is derived for posterior inference in negative binomial models with unobserved parameter heterogeneity and spatial dependence. Pólya-Gamma augmentation is used to deal with the non-conjugacy of the negative binomial likelihood and an integrated non-factorised specification of the variational distribution is adopted to capture posterior dependencies. The benefits of the proposed approach are demonstrated in a Monte Carlo study and an empirical application on estimating youth pedestrian injury counts in census tracts of New York City. The VB approach is around 45 to 50 times faster than MCMC on a regular eight-core processor in a simulation and an empirical study, while offering similar estimation and predictive accuracy. Conditional on the availability of computational resources, the embarrassingly parallel architecture of the proposed VB method can be exploited to further accelerate its estimation by up to 20 times.
  • Journal article
    Kutela B, Langa N, Mwende S, Kidando E, Kitali AE, Bansal Pet al., 2021,

    A text mining approach to elicit public perception of bike-sharing systems

    , TRAVEL BEHAVIOUR AND SOCIETY, Vol: 24, Pages: 113-123, ISSN: 2214-367X
  • Journal article
    Kazemzadeh K, Bansal P, 2021,

    Electric bike navigation comfort in pedestrian crowds

    , SUSTAINABLE CITIES AND SOCIETY, Vol: 69, ISSN: 2210-6707
  • Journal article
    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.
  • Journal article
    Bansal P, Dua R, Krueger R, Graham DJet al., 2021,

    Fuel economy valuation and preferences of Indian two-wheeler buyers

    , JOURNAL OF CLEANER PRODUCTION, Vol: 294, ISSN: 0959-6526
  • Journal article
    Vickerman R, 2021,

    Will Covid-19 put the public back in public transport? A UK perspective

    , TRANSPORT POLICY, Vol: 103, Pages: 95-102, ISSN: 0967-070X
  • Journal article
    Li H, Zhu M, Graham DJ, Ren Get al., 2021,

    Evaluating the speed camera sites selection criteria in the UK

    , Journal of Safety Research, Vol: 76, Pages: 90-100, ISSN: 0022-4375

    Introduction: Speed cameras have been implemented to improve road safety over recent decades in the UK. Although the safety impacts of the speed camera have been estimated thoroughly, the criteria for selecting camera sites have rarely been studied. This paper evaluates the current speed camera sites selection criteria in the UK based on safety performance. Method: A total of 332 speed cameras and 2,513 control sites with road traffic accident data are observed from 2002 to 2010. Propensity score matching method and empirical Bayes method are employed and compared to estimate the safety effects of speed cameras under different scenarios. Results: First, the main characteristics of speed cameras meeting and not meeting the selection criteria are identified. The results indicate that the proximity to school zones and residential neighborhoods, as well as population density, are the main considerations when selecting speed camera sites. Then the official criteria used for selecting camera sites are evaluated, including site length (a stretch of road that has a fixed speed camera or has had one in the past), previous accident history, and risk value (a numerical scale of the risk level). The results suggest that a site length of 500 m should be used to achieve the optimum safety effects of speed cameras. Furthermore, speed cameras are most effective in reducing crashes when the requirement of minimum number of historical killed and seriously injured collisions (KSIs) is met. In terms of the risk value, it is found that the speed cameras can obtain optimal effectiveness with a risk value greater than or equal to 30, rather than the recommended risk value of 22.
  • Journal article
    Graham D, Carbo J, 2020,

    Quantifying the impacts of air transportation on economic productivity: a quasi-experimental causal analysis

    , Economics of Transportation, Vol: 24, ISSN: 2212-0122

    Air transport capacity expansions are often justified on the grounds that they will improve economic perfor-mance and induce growth. Such causal impacts are hard to identify empirically due to the fundamentally endogenous nature of the relationship between air transport and the economy. This paper contributes to the empirical literature on aviation-economy effects by conducting a case study of the impacts of air transportation activity on productivity in Chinese provinces. For exogenous variation we exploit a policy scenario created by the 2003 deregulation of the Chinese aviation sector, which was applied in all provinces of China except Beijing and Tibet. We find that this policy intervention resulted in substantial growth in air transport passengers and cargo. We estimate the causal effect of air transport on productivity by comparing GDP per employee in Tibet relative to a synthetic control region affected by the deregulation policy. We find a significant positive productivity effect from aviation expansion following the 2003 deregulation. Use of a differences-in-differences specification con-firms this result
  • Journal article
    Ma L, Graham DJ, Stettler MEJ, 2021,

    Air quality impacts of new public transport provision: A causal analysis of the Jubilee Line Extension in London

    , Atmospheric Environment, Vol: 245, Pages: 118025-118025, ISSN: 1352-2310

    Public transport is commonly associated with benefits such as reducing road traffic congestion and improving air quality. This paper focuses on evaluating the causal impact of a new public transport provision in London, the Jubilee Line Extension (JLE) in 1999, on air quality. Using meteorological normalisation and a regression discontinuity design with time as the forcing variable, we show that the JLE led to only small changes in air pollution at some specific locations; detectable changes in NOx, NO2, and O3 concentrations were found at 63%, 43% and 29% of air pollution monitoring sites, respectively. For those sites where a change in pollution was detected, the responses ranged from −2% to +1% for NO2 and -1% to 0% for O3. We calculate that the long-run effects are greater, ranging from −11% to +3% for NO2 and from −2% to +2% for O3 at sites that showed a response to the JLE. Aggregating across all sites in London for a city-wide effect, both short and long-run effects were less than 1% or insignificant. We find statistically significant increases in NO2 and O3 concentrations at some background sites, but the magnitude of effect is within +1% in the short-run and +3% in the long-run. Our analysis shows that the effect of the JLE on air pollution in some areas was greater than others, however across London the effect was small and this indicates that public transport provision on its own is not an effective strategy to improve air quality.
  • Journal article
    Singh R, Graham DJ, Anderson RJ, 2020,

    Quantifying the effects of passenger-level heterogeneity on transit journey times

    , Data-Centric Engineering, Vol: 1, Pages: e15-1-e15-28, ISSN: 2632-6736

    In this paper we apply flexible data-driven analysis methods on large scale mass transit data to identify areas for improvement in the engineering and operation of urban rail systems. Specifically, we use data from automated fare collection (AFC) and automated vehicle location (AVL) systems to obtain a more precise characterisation of the drivers of journey time variance on the London Underground, and thus an improved understanding of delay. Total journey times are decomposed via a probabilistic assignment algorithm and semiparametric regression is undertaken to disentangle the effects of passenger-specific travel characteristics from network related factors. For total journey times, we find that network characteristics, primarily train speeds and headways, represent the majority of journey time variance. However, within the typically twice as onerous access and egress time components, passenger-level heterogeneity is more influential. On average, we find that intra-passenger heterogeneity represents 6% and 19% of variance in access and egress times, respectively, and that inter-passenger effects have a similar or greater degree of influence than static network characteristics. The analysis shows that while network-specific characteristics are the primary drivers journey time variance in absolute terms, a non-trivial proportion of passenger-perceived variance would be influenced by passenger-specific characteristics. The findings have potential applications related to improving the understanding of passenger movements within stations, for example, the analysis can be used to assess the relative way-finding complexity of stations, which can in turn guide transit operators in the targeting of potential interventions.
  • Journal article
    Bansal P, Liu Y, Daziano R, Samaranayake Set al., 2020,

    Impact of discerning reliability preferences of riders on the demand for mobility-on-demand services

    , Transportation Letters, Vol: 12, Pages: 677-681, ISSN: 1942-7867

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