Validating and improving public transport origin–destination estimation algorithm using smart card fare data

Smart card data are increasingly used for transit network planning, passengers’ behaviour analysis and network demand forecasting. Public transport origin–destination (O–D) estimation is a significant product of processing smart card data. In recent years, various O–D estimation methods using the trip-chaining approach have attracted much attention from both researchers and practitioners. However, the validity of these estimation methods has not been extensively investigated. This is mainly because these datasets usually lack data about passengers’ alighting, as passengers are often required to tap their smart cards only when boarding a public transport service. Thus, this paper has two main objectives. First, the paper reports on the implementation and validation of the existing O–D estimation method using the unique smart card dataset of the South-East Queensland public transport network which includes data on both boarding stops and alighting stops. Second, the paper improves the O–D estimation algorithm and empirically examines these improvements, relying on this unique dataset. The evaluation of the last destination assumption of the trip-chaining method shows a significant negative impact on the matching results of the differences between actual boarding/alighting times and the public transport schedules. The proposed changes to the algorithm improve the average distance between the actual and estimated alighting stops, as this distance is reduced from 806 m using the original algorithm to 530 m after applying the suggested improvements.     

Carbon emission from urban passenger transportation in Beijing

Urban passenger transport significantly contributes to global greenhouse gas emissions, especially in developing countries owing to the rapid motorization, thus making it an important target for carbon reduction. This article established a method to estimate and analyze carbon emission from urban passenger transport including cars, rail transit, taxis and buses. The scope of research was defined based on car registration area, transport types and modes, the stages of rail transit energy consumption. The data availability and gathering were fully illustrated. A city level emission model for the aforementioned four modes of passenger transport was formulated, and parameters including emission factor of electricity and fuel efficiency were tailored according to local situations such as energy structure and field survey. The results reveal that the emission from Beijing’s urban passenger transport in 2012 stood at 15 million tonnes of CO₂, of which 75.5% was from cars, whereas car trip sharing constitutes only 42.5% of the total residential trips. Bus travel, yielding 28.6 g CO₂, is the most efficient mode of transport under the current situations in terms of per passenger kilometer (PKM) emission, whereas car or taxi trips emit more than 5 times that of bus trips. Although a decrease trend appears, Beijing still has potential for further carbon reduction in passenger transport field in contrast to other cities in developed countries. Development of rail transit and further limitation on cars could assist in reducing 4.39 million tonnes CO₂ emission.     

Effects of e-bikes on bicycle use and mode share

In Norway, as in many countries, a political goal is to increase bicycle use, and the e-bike is promising in this respect. However, concerns have been raised about mode-share effects. It has been argued that if the e-bike’s only function is in cycling becoming cycling with electric assistance, there would be no benefit to either the environment or public health. Little is yet known about the use of the e-bike, or of its potential in reducing motorized travel. In the current study, 66 randomly selected participants were given an e-bike to use for a limited period of time and the results compared with those of a control group (N = 160). E-bike cycling trips increased from 0.9 to 1.4 per day, distance from 4.8 km to 10.3 km and, as a share of all transport, from 28% to 48%, whereas with the control group there was no increase in cycling. The effect of the e-bike increased with time, indicating a learning effect among users, and was greater for female than for male cyclists. There were no differences with age. Overall, the results suggest that the e-bike is indeed practical for everyday travel.     

Do modes of transportation and GPS affect cognitive maps of Londoners?

Legibility has long been recognized as an important factor in creating a good image of a city in individuals’ minds. This image is perceived to assist people in understanding the city, finding their way, and recalling the city. The quality of the image affects individuals’ abilities in way-finding. This is especially important for cosmopolitan and global cities such as London in order to preserve resources and time, manage travel costs, limit pollution (air or noise) and enhance these cities as places to live, work and visit. This research examines the cognitive maps of London drawn by a sample of its residents to discover how different modes of transportation and GPS usage could affect individuals’ urban images. Such research is useful for town planners, local government departments, and urban and transport planners because of the way it considers the legibility of London as and provides a tool to study individuals’ urban images. 101 participants were recruited with at least a two-year residency from both genders (38.6% females and 61.4% males) with the average age of 33.88 and S.D. = 10.63. The results suggest car use has a positive correlation with seeing London in city scale and GPS usage has a negative correlation. Whilst recent studies have shown that there are differences between active travel modes (e.g., walking, bicycle riding or driving a car) and passive modes (e.g., as a passenger taking a bus, train or taxi), this study indicates that GPS usage also influences cognitive maps, with a negative correlation found between GPS usage and drawing maps on a city scale. Other significant associations were found for the car drivers with a positive relation with the number of roads mentioned on the maps, seeing London in city scale and having a two-dimensional façade image of the city in mind.     

A forecast air pollution model applied to a hypothetical urban road pricing scheme: An empirical study in Madrid

Reducing the air pollution from increases in traffic congestion in large cities and their surroundings is an important problem that requires changes in travel behavior. Road pricing is an effective tool for reducing air pollution, as reflected currently urban road pricing outcomes (Singapore, London, Stockholm and Milan). A survey was conducted based on establishing a hypothetical urban road pricing system in Madrid (a random sample size n = 1298). We developed a forecast air pollution model with time series analysis to evaluate the consequences of possible air pollution decreases in Madrid. Results reveal that the hypothetical road pricing for Madrid could have highly significant effects on decreasing air pollution outside of the city and in the inner city during the peak operating time periods of maximum congestion (morning peak hours from 7:00 to 10:00 and evening peak hours from 18:00 to 20:00). Furthermore, this system could have significant positive effects on a shift toward using public transport and non-motorized modes inside the hypothetical toll zone. This reveals that the system has a high capacity to motivate a decrease in air pollution and impose more sustainable behavior for public transport users.     

A point card system for public transport utilization in Korea

This study analyzes consumer preferences for a new incentive program based on a point card to promote green consumption; the study also examines the program’s impact on bus utilization in South Korea. An ex-ante analysis was conducted to examine how consumer behavior can be modified based on varying incentive levels of the point card system. In addition, the effect of the system on consumers’ public transport utilization and resulting CO₂ emissions reductions are analyzed. The adoption probability of the point card is forecast at about 93%, and annual CO₂ emissions are forecast to decrease by 610 kt CO₂.     

Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks

Reliable and accurate short-term subway passenger flow prediction is important for passengers, transit operators, and public agencies. Traditional studies focus on regular demand forecasting and have inherent disadvantages in predicting passenger flows under special events scenarios. These special events may have a disruptive impact on public transportation systems, and should thus be given more attention for proactive management and timely information dissemination. This study proposes a novel multiscale radial basis function (MSRBF) network for forecasting the irregular fluctuation of subway passenger flows. This model is simplified using a matching pursuit orthogonal least squares algorithm through the selection of significant model terms to produce a parsimonious MSRBF model. Combined with transit smart card data, this approach not only exhibits superior predictive performance over prevailing computational intelligence methods for non-regular demand forecasting at least 30 min prior, but also leverages network knowledge to enhance prediction capability and pinpoint vulnerable subway stations for crowd control measures. Three empirical studies with special events in Beijing demonstrate that the proposed algorithm can effectively predict the emergence of passenger flow bursts.     

The effects of a compressed working week on commuters’ daily activity patterns

This study was based on 220 randomly selected government employees of the University of the Philippines in Los Baños, and was carried out after the Philippine government implemented an experimental two-month compressed working week scheme. The study aimed to examine compressed working week-related lifestyle pattern changes, especially changes in activity–time patterns. The moderating effects of compressed working week to the commuters were also explored. Survey results showed that two-hour increases in the compressed working week workday substantially impacted activity–travel patterns. Commuters reduced household activities by about one hour, sleeping time by about 20 minutes, and pre-work preparation time by about 30 minutes. Commuting times also significantly declined during the compressed working week. This study also identified factors that moderated the compressed working week plan’s effects on activity–travel patterns.     

Characterising group-cycling journeys using interactive graphics

The group-cycling behaviours of over 16,000 members of the London Cycle Hire Scheme (LCHS), a large public bikeshare system, are identified and analysed. Group journeys are defined as trips made by two or more cyclists together in space and time. Detailed insights into group-cycling behaviour are generated using specifically designed visualization software. We find that in many respects group-cycle journeys fit an expected pattern of discretionary activity: group journeys are more likely at weekends, late evenings and lunchtimes; they generally take place within more pleasant parts of the city; and between individuals apparently known to each other. A separate set of group activity is found, however, that coincides with commuting peaks and that appears to be imposed onto LCHS users by the scheme’s design. Studying the characteristics of individuals making group journeys, we identify a group of less experienced LCHS cyclists that appear to make more spatially extensive journeys than they would do normally while cycling with others; and that female cyclists are more likely to make late evening journeys when cycling in groups. For 20% of group cyclists, the first journey ever made through the LCHS was a group journey; this is particularly surprising since just 9% of all group cyclists’ journeys are group journeys. Moreover, we find that women are very significantly (p < 0.001) overrepresented amongst these ‘first time group cyclists’. Studying the bikeshare cyclists, or bike share ‘friends’, that individuals make ‘first time group journeys’ with, we find a significantly high incidence (p < 0.001) of group journeys being made with friends of the opposite gender, and for a very large proportion (55%) of members these first ever journeys are made with a friend that shares the same postcode. A substantial insight, then, is that group cycling appears to be a means through which early LCHS usage is initiated.     

Electric vehicles in multi-vehicle households

The suitability of an electric vehicle of a given range to serve in place of a given conventional vehicle is not limited by the daily travel over distances within that that range, but rather by the occasional inconvenience of finding alternative transport for longer trips. While the frequency of this inconvenience can be computed from usage data, the willingness of individual users to accept that replacement depends on details of available transportation alternatives and their willingness to use them. The latter can be difficult to assess. Fortunately, 65% of US households have access to the most convenient alternative possible: a second car. In this paper we describe an analysis of prospective EV acceptance and travel electrification in two-car households in the Puget Sound region. We find that EVs with 60 miles of useful range could be acceptable (i.e. incur inconvenience no more than three days each year) to nearly 90% of two-car households and electrify nearly 55% of travel in those households (32% of all travel). This compares to 120 miles range required to achieve the same fraction of electrified travel via one-for-one replacement of individual vehicles. Even though only one third of personal vehicles in the US may be replaced in this paradigm, the ‘EV as a second-car’ concept is attractive in that a significant fraction of travel can be electrified by vehicles with modest electric range and virtually no dependence on public charging infrastructure.     

Estimation of a route choice model for urban public transport using smart card data

This paper describes a logit model of route choice for urban public transport and explains how the archived data from a smart card-based fare payment system can be used for the choice set generation and model estimation. It demonstrates the feasibility and simplicity of applying a trip-chaining method to infer passenger journeys from smart card transactions data. Not only origins and destinations of passenger journeys can be inferred but also the interchanges between the segments of a linked journey can be recognised. The attributes of the corresponding routes, such as in-vehicle travel time, transfer walking time and to get from alighting stop to trip destination, the need to change, and the time headway of the first transportation line, can be determined by the combination of smart card data with other data sources, such as a street map and timetable. The smart card data represent a large volume of revealed preference data that allows travellers' behaviour to be modelled with higher accuracy than by using traditional survey data. A multinomial route choice model is proposed and estimated by the maximum likelihood method, using urban public transport in ?ilina, the Slovak Republic, as a case study     

Mining and correlating traffic events from human sensor observations with official transport data using self-organizing-maps

Cities are complex systems, where related Human activities are increasingly difficult to explore within. In order to understand urban processes and to gain deeper knowledge about cities, the potential of location-based social networks like Twitter could be used a promising example to explore latent relationships of underlying mobility patterns. In this paper, we therefore present an approach using a geographic self-organizing map (Geo-SOM) to uncover and compare previously unseen patterns from social media and authoritative data. The results, which we validated with Live Traffic Disruption (TIMS) feeds from Transport for London, show that the observed geospatial and temporal patterns between special events (r = 0.73), traffic incidents (r = 0.59) and hazard disruptions (r = 0.41) from TIMS, are strongly correlated with traffic-related, georeferenced tweets. Hence, we conclude that tweets can be used as a proxy indicator to detect collective mobility events and may help to provide stakeholders and decision makers with complementary information on complex mobility processes.     

Population exposure to ultrafine particles: Size-resolved and real-time models for highways

Prior research on ultrafine particles (UFP) emphasizes that concentrations are especially high on-highway, and that time on highways contribute disproportionately to total daily exposures. This study estimates individual and population exposure to ultra-fine particles in the Minneapolis – St. Paul (Twin Cities) metropolitan area, Minnesota. Our approach combines a real-time model of on-highway size-resolved UFP concentrations (32 bins, 5.5–600 nm); individual travel patterns, derived from GPS travel trajectories collected in 144 individual vehicles (123 h at locations with UFP estimates among 624 vehicle-hours of travel); and, loop-detector data, indicating real-time traffic conditions throughout the study area. The results provide size-resolved spatial and temporal patterns of exposure to UFP among freeway users. On-highway exposures demonstrate significant variability among users, with highest concentrations during commuting peaks and near highway interchanges. Findings from this paper could inform future epidemiological studies in on-road exposure to UFP by linking personal exposures to traffic conditions.     

The effects of physical activity on greenhouse gas emissions for common transport modes in European countries

This paper applies a life cycle methodology to estimate activity-related contributions of transport modes to GHG emissions. The methodology uses national input–output tables, environmental accounts, household budget data and nutritional data to derive food-sector GHG coefficients of consumption for ten European countries. The food energy requirements for each mode of transport are estimated taking account of the modal activity level and energy requirements. Typical national food energy-related emissions for walking, cycling, and driving ranged from 25.6 to 77.3 gCO₂-eq/pass.km, 10.4–31.4 gCO₂-eq/pass.km and 1.7–5.2 gCO₂-eq/pass.km; passenger transport was found to result in no food-related emissions above those for a resting individual. Emissions vary between countries depending on the emissions intensities of their energy sectors as well as food prices and average body weights. A life cycle assessment of modal emissions in the UK is undertaken using the food-energy emissions intensities estimated and car travel was found to have the highest emissions intensity, followed by bus, cycling and walking.     

Would personal carbon trading reduce travel emissions more effectively than a carbon tax?

The application of personal carbon trading (PCT) to transport choices has recently been considered in the literature as a means of reducing CO₂ emissions. Its potential effectiveness in changing car travel behavior is compared to the conventional carbon tax (CT) by means of a stated preferences survey conducted among French drivers (N ∼ 300). We show evidence that PCT could effectively change travel behavior and hence reduce transport emissions from personal travel. There is however a definite reluctance to reduce car travel. We were unable to demonstrate any significant difference between the effectiveness of PCT and the CT with regard to changing travel behavior. However, in the experiment, the PCT scheme provided consistent results while this was not the case for the CT scheme. Further research is needed into the “social norm” conveyed by a personal emissions allowance.     

Ultrafine particle infiltration into passenger vehicles. Part II: Model analysis

Experimental studies showed that infiltration and passive ventilation are important air exchange mechanisms inside vehicles but previous mathematical models did not consider either one. In this study, we incorporated infiltration and passive ventilation to advance the existing mathematical models and evaluated how different transport mechanisms affect passenger exposures at increasing speeds. Infiltration was formulated using Bernoulli’s equation and passive ventilation was derived empirically. The new model describes ultrafine particle (UFP) and carbon dioxide (CO₂) transport for a wide range of driving speed under any ventilation conditions. Unlike statistical models, this mathematical model can also provide vehicle-specific and transport mechanism-specific information. The model predictions were in a good agreement with data collected from 10 different vehicle models with an average discrepancy of less than 16% for UFPs and less than 3% for CO₂. Under outdoor air (OA) mode, when the fan is off, the model simulation showed that the infiltration and passive ventilation can substantially increase the UFP I/O (in-cabin/on-road concentrations) ratio from 0.15 at 0 km/h to 0.57 at 130 km/h. At medium fan setting, mechanical ventilation dominates and UFP I/O stays at 0.58 regardless of driving speed. Under recirculation (RC) mode, infiltration increases and the RC-mode filtration only removed 44% and 69% of the infiltrated particles at the lowest and medium fan settings, respectively. Model simulations under OA mode show that infiltration starts to occur above 115 km/h with the lowest fan setting; whereas, medium and higher fan settings prevent infiltration up to 145 km/h.     

Evaluating the social cost of cruise ships air emissions in major ports of Greece

Detailed NO_x, SO₂ and PM_2.5 emissions have been estimated for cruise ships in the five busiest Greek ports (i.e. Piraeus, Santorini, Mykonos, Corfu and Katakolo) for year 2013. The emissions were analyzed in terms of gas species, seasonality and activity. The total in-port inventory of cruise shipping accounted to 2742.7 tons: with NO_x being dominant (1887.5 tons), followed by SO₂ and PM_2.5 (760.9 and 94.3 tons respectively). Emissions during hotelling corresponded to 88.5% of total and have significantly outweighed those produced during ships’ maneuvering activities (11.5% of total). Seasonality was found to play a major role, as summer emissions and associated impacts were significantly augmented. The anticipated health impacts of ship emissions can reach to €24.3 million or to €5.3 per passenger proving the necessity of control of the emissions produced by cruise ships in port cities or policy and measures towards a more efficient cruise industry.     

Commuters’ exposure to PM2.5 and CO2 in metro carriages of Shanghai metro system

Air quality inside transportation carriages has become a public concern. A comprehensive measurement campaign was conducted to examine the commuters’ exposure to PM_2.5 (d_p ⩽ 2.5 μm) and CO₂ in Shanghai metro system under different conditions. The PM_2.5 and CO₂ concentrations inside all the measured metro lines were observed at 84 ± 42 μg/m³ and 1253.1 ± 449.1 ppm, respectively. The factors that determine the in-carriage PM_2.5 and CO₂ concentrations were quantitatively investigated. The metro in-carriage PM_2.5 concentrations were significantly affected by the ventilation systems, out-carriage PM_2.5 concentrations and the passenger numbers. The largest in-carriage PM_2.5 and CO₂ concentrations were observed at 132 μg/m³ and 1855.0 ppm inside the carriages equipped with the oldest ventilation systems. The average PM_2.5 and CO₂ concentrations increased by 24.14% and 9.93% as the metro was driven from underground to overground. The average in-carriage PM_2.5 concentrations increased by 17.19% and CO₂ concentration decreased by 16.97% as the metro was driven from urban to the suburban area. It was found that PM_2.5 concentration is proportional to the on-board passenger number at a ratio of 0.4 μg/m³·passenger. A mass-balance model was developed to estimate the in-carriage PM_2.5 concentration under different driving conditions.     

The value of service reliability

This paper studies the impact of service frequency and reliability on the choice of departure time and the travel cost of transit users. When the user has (α, β, γ) scheduling preferences, we show that the optimal head start decreases with service reliability, as expected. It does not necessarily decrease with service frequency, however. We derive the value of service headway (VoSH) and the value of service reliability (VoSR), which measure the marginal effect on the expected travel cost of a change in the mean and in the standard deviation of headways, respectively. The VoSH and the VoSR complete the value of time and the value of reliability for the economic appraisal of public transit projects by capturing the specific link between headways, waiting times, and congestion. An empirical illustration is provided, which considers two mass transit lines located in the Paris area.     

Competitive tendering versus performance-based negotiation in Swiss public transport

The purpose of this study is to assess differences in the levels of cost efficiency of bus lines operated under competitively tendered contracts and performance-based negotiated contracts. Following the revision of the Swiss railways act in 1996, regional public authorities were given the choice between two different contractual regimes to procure public passenger transport services. We directly compare the impact of competitive tendering and performance-based negotiation by applying a stochastic frontier analysis to the complete dataset of bus lines (n = 630) operated by the main Swiss company (Swiss Post) at the same time (in 2009) throughout the country. The overall results show that the differences in the levels of cost efficiency between the two contractual regimes are not significant. Our findings are in line with recent evidence of cost convergence between competitive tendering and performance-based negotiation, and suggest that the practice of using both contractual regimes is challenging for the operators in terms of competitive pressure. The threat of competitive tendering may have a disciplining effect on negotiation since it prevents bus companies from bargaining inadequate rents and inducing asymmetric information advantages.