•Public transport passengers use at least two type of route choice strategies.•48.8% of passengers use common lines or aggregated strategy for route choice.•51.2% of passengers use disaggregated ...strategy for route choice.•The latent class models allow capturing heterogeneity in route choice strategy.•Smart card data allows studying the passenger route choice behavior.
We contribute to the understanding of public transport passenger route choice behavior by developing and applying methods that capture behavioral strategies by making use of smart card data. We begin by proposing the classification of possible route choice behavioral strategies in two groups: disaggregated strategies and aggregated strategies. In the former, the alternatives correspond to itineraries, which are fixed sequences of stops and public transport lines. In the latter, common line alternatives are considered, which are combinations of itineraries defined under given criteria. Almost all route choice models use consideration sets composed only of itineraries, while public transport assignment models for strategic analysis mostly use a version of the common lines approach. We postulate that this dichotomy is inappropriate and that, instead, heterogeneity exists in the route choice strategy, both between users and across contexts. With the aim of verifying this hypothesis, we first propose an indicator function constructed as the difference between expected and observed trips for a given behavioral assumption. We apply then the indicator to a case study based on smart card data from the city of Santiago, Chile, from which we find evidence of heterogeneity. We identify individuals that follow either an aggregated or a disaggregated strategy, as well as others who seem to be using a combination of both strategies. We further analyze the heterogeneity hypothesis using an integrated discrete choice and latent class approach, which we apply to the same case study. This approach involves estimating path-size logit models built with alternatives from disaggregated and aggregated strategies, as well as a latent class model built from a combination of both. It also addresses methodological challenges related to the definition of the consideration set and the correction of endogeneity. Results confirm the heterogeneity hypothesis, suggesting that the mean probability that passengers belong to the class that uses a disaggregated strategy for route choice is 51.2%, and that this heterogeneity markedly occurs between individuals, not within their choices. We also find that the latent class considering aggregated alternatives appear to prefer bus over metro, while the latent class considering disaggregated alternatives prefer metro over bus. The fact that waiting time is relatively more burdensome for the aggregated strategy class is in line with this class’s preference for common lines. Walking time and bus crowding are more burdensome for the disaggregated strategy class, in line with their observed modal preferences.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Accurately predicting short-term passenger flow is essential to optimize operation resources and improve transportation services in urban metro systems. However, it has become a challenging problem ...due to spatial-temporal demand fluctuations and heterogeneous passengers’ travel behavior, i.e., the interaction between the departure and arrival passengers. In this paper, we develop an explainable Stacking-Catboost model for passenger flow prediction combining the passenger’s return probability computation. The model explores several basic ensemble learning models and the best stacking strategy. To better characterize the macroscopic spatiotemporal travel patterns other than the micro individual travel behavior, several relevant variables such as train operation characteristics, the nearby bus stations, and points of interest are considered. Ablation studies are conducted to investigate the utility of each component of the proposed model. An explainable analysis is performed to interpret information from “black box” models and quantify the contribution of each feature. We present a real-world case study conducted in the Beijing metro, demonstrating that our proposed method achieves significant improvements over existing techniques in hourly prediction tasks. Specifically, our approach outperforms the CategoricalBoosting (CatBoost) by up to 11.11 % and the Random Forest (RF) by up to 12.90 %, showcasing the effectiveness of incorporating macroscopic spatiotemporal travel patterns and micro individual travel behavior to enhance short-term forecasting accuracy. The global interpretability of models reveals key factors that impact performance, such as returning passenger flow, historical travel demand, and timetable data. Our results offer valuable insights into short-term forecasting challenges and provide a framework for leveraging explainable artificial intelligence to improve transportation services in urban metro systems.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This study identifies the relationship between individual's travel and their perceived Quality of Life (QoL). We consider two variables for travel reflecting its objective/quantitative and subjective ...dimensions. The observed one is a measure based on public transport trip frequency and travel duration obtained from smart card data. The second is the perceived level of Travel Satisfaction which is obtained by survey data. This study is conducted in Shizuoka city, a mid-size city in Japan where we could obtain and link both data sets. We focus particularly on aging effects and therefore divide the data samples into three groups, non-elderly (less than 65 year), younger-old (65–74 years), and older-old (over 75 years). Older people show in general higher travel satisfaction. Regression analysis with travel satisfaction as dependent variable indicate the importance of travel opportunity for older-old. We then identify the relative importance ofmobility on QoL considering both objective and subjective one and discuss transport policy implications for an aging society based on these results. The results provide support that public transport systems are determinants of its usage, travel satisfaction and eventually QoL. These relationships are most evident for younger-old but less significant for older-old.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Few studies based on large sample data have examined mobility patterns from a travel distance perspective and investigated the potential influence of urban form and land use on people's daily travel ...distances. This paper provides additional empirical insights into spatiotemporal urban mobility patterns and their relationship with urban form and land use using station-based average travel distances (ATDs). Drawing on smart card data of the Nanjing metro system, land use data and open source points-of-interest (POIs) data, we apply exploratory spatial data and quantile regression analysis to examine distance patterns and explore the potential effects of urban form and land use calculated at different spatial scales (i.e. 800 m, 2 km and 5 km) on the ATDs. By comparing mobility patterns between weekdays and weekends and for different times of day, our findings highlight that ATDs are not uniformly nor randomly distributed in space. Positive spatial autocorrelation is found for different time segments. The results of OLS and quantile regression models show a positive and robust relationship between ATDs and distances to the city center (DCs). The models also prove that land use mix (especially measured at the 2 km and 5 km scale) significantly affects ATDs, supporting the importance of land use mix in decreasing daily travel distances. No significant relationship is found between ATDs and distances to the nearest subsidiary center (DSCs), while the employment/entertainment-residence balance has a marginal effect on ATDs at relatively large spatial scales (i.e. 2 km, 5 km). Consequently, with respect to reducing the ATDs, we recommend enhancing land use mix and reducing the imbalance between employment/entertainment and residence at larger spatial scales. Potential applications and future research directions are discussed. The findings in the present paper are helpful for guiding urban planning and policy making.
•We explored urban mobility rhythm using the station-based average travel distances (ATDs) of out-flows, in-flows and all-flows for a whole day and a specific time span within a day.•ATDs are not uniformly or randomly distributed in space and show the pattern of concentric rings from the central to peripheral urban area.•Quantile regression models were used in the study to investigate the relationships between distances to the city center, distances to the nearest subsidiary center, land use mix, POIs-based employment/entertainment-residence balance and ATDs.•Land use mix has a significantly positive impact on ATDs.•We recommend enhancing land use mix and reducing the imbalance between employment/entertainment and residence at larger spatial scales.
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The rapid increase in user base and technological penetration has enabled the use of a wide range of devices and applications. The services are rendered to these devices from single-server or highly ...distributed server environments, irrespective of their location. As the information exchanged between servers and clients is private, numerous forms of attacks can be launched to compromise it. To ensure the security, privacy, and availability of the services, different authentication schemes have been proposed for both single-server and multi-server environments. The primary performance objective of such schemes is to prevent most (if not all) attacks, with minimal computational costs at the server and user ends. To address this challenge, this paper presents a secure user authentication scheme with anonymity (SUAA) for single-server and multi-server environments. It works on 3-factor authentication, involving passwords, smart cards, and biometric data. We use symmetric and asymmetric encryption for single-server and multi-server architectures respectively, to reduce the computational costs. Through a comprehensive security analysis, we show that the proposed scheme is reliable through mutual authentication, and is resilient to attacks addressed by state of the art solutions. Time cost analysis also shows less time required to complete the authentication process.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The potential role of smart card data for travel behaviour analysis is considered. Case studies of smart card experience in Britain are examined, of the pensioner concessionary pass in Southport, ...Merseyside, and the commercially operated scheme in Bradford. The nature of smart card data puts an emphasis on concept definition and rules-based processing. Using smart card data, estimates may be made of card turnover rates, trip rates per card on issue, and inferences made of the proportion of linked trips. In comparison with existing data sources, much larger samples may be obtained, and behaviour analysed over much longer periods. However, there are limitations, mainly that trip length is not recorded on systems based on validating cards only on entry to a bus, and that certain types of data still require direct survey methods for their collection (such as journey purpose). Hence, a complementary role may be identified for smart card data vis a vis existing data collection methods, rather than entirely superseding them. Transport service providers should also ensure that their smart card retailing strategy does not undermine the quality of the data in the initial years of the scheme, therefore limiting its use during this period.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
27.
Frama-C Cuoq, Pascal; Kirchner, Florent; Kosmatov, Nikolai ...
Software Engineering and Formal Methods
Book Chapter
Peer reviewed
Open access
Frama-C is a source code analysis platform that aims at conducting verification of industrial-size C programs. It provides its users with a collection of plug-ins that perform static analysis, ...deductive verification, and testing, for safety- and security-critical software. Collaborative verification across cooperating plug-ins is enabled by their integration on top of a shared kernel and datastructures, and their compliance to a common specification language. This foundational article presents a consolidated view of the platform, its main and composite analyses, and some of its industrial achievements.
•Estimated the causal economic impact of an off-peak fare discount in Hong Kong.•Applied the difference-in-difference method to control for confounding bias.•Found the aggregate effect to be small ...but statistically significant and heterogeneous.•Responsiveness was governed by travel costs and crowding.•Produced novel causal estimates of trip rescheduling elasticities.
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.
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Many public transport networks worldwide experience high crowding levels. Overcrowding can result in passengers not able to board the first arriving vehicle. We infer how waiting time induced by ...being denied boarding in crowded public transport systems is valued by passengers, based on observed passenger route choice behaviour. For this purpose, we estimate a revealed preference route choice model based on passenger and vehicle movement data. As denied boarding typically occurs only at specific locations and within strict time bands, whilst its occurrence is notoriously uncertain, we propose additional constraints to generate an appropriate choice set for which observed route choices can be used to estimate denied boarding perceptions. We found that the additional waiting time caused by denied boarding is valued 68% more negatively compared to the initial waiting time. On average, one minute of initial and denied boarding wait time are perceived as 1.62 and 2.72 min on-board an uncrowded vehicle, respectively. Not incorporating this more negative denied boarding wait time valuation can result in an underestimation of the passenger and societal impact of overcrowding in public transport systems. Moreover, it can underestimate the benefits of potential crowding relief measures and as such underestimate the benefit-cost ratio of these measures.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Passenger flow distribution in the metro system is fundamental for many applications such as network planning and design, passenger flow forecasting, individual travel activity modeling and emergency ...response management. However, in most metro systems the smart card automated fare collection (AFC) equipment in Beijing only record when and where a passenger enters and leaves the metro network. Therefore, how to accurately determine passenger flow distribution in unknown travel routes remains a challenging task for the managers. This paper presents a methodology for reconstructing metro passenger flow distribution from large-scale smart card data. A clustering method was first applied to group the travel time of passengers between origin–destination (OD) station pairs into different clusters. Then an approach was proposed that considered both uncertain walking time and transfer time, to estimate the theoretical travel time of all possible routes between the OD pair. An approach to measure the similarity was further employed to match each travel time cluster to a most-likely travel route, and finally obtained the passengers’ flow of every route. Compared with two classical methods, the proposed approach was more accurate and efficient.
•A data-driven methodology is proposed to obtain metro passenger flow distributions.•A similarity measure model is built to identify possible routes by clustering result.•The travel times are clustered to obtain the possible travel time groups.•Real data from the Beijing metro smart card system is applied to verify our approach.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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