Cluster Analysis for Diminishing Heterogeneous Opinions of Service Quality Public Transport Passengers

[EN] One of the principal measures that public transport administrations are following for reaching a sustainable transportation in the cities consists on attract a higher number of citizens towards the use of public transport modes, by offering high quality services. Collecting users opinions is the best way of detecting where the service is failing and which aspects are been provided successfully. The main problem that has to be faced for analyzing service quality is the subjective nature of its measurement, offering heterogeneous assessments among passengers about the service. Stratifying the sample of users on segments of passengers which have more uniform opinions about the service can help to reduce this heterogeneity. This stratification usually is conducted based on the social and demographic characteristics of the passengers. However, there are more advance techniques that permits to identify more homogeneous groups of users. One of these techniques is the Cluster Analysis, which is a data mining technique that can be used for segmenting the sample of passengers on groups that share some common characteristics, and that have more homogeneous perceptions about the service. This technique has been applied in other fields of transport engineering but it has never been applied for searching homogeneous groups of users with regards to service quality evaluation in a public transport service. For this reason, the aim of this work is to find groups of passengers that perceive the quality of the service in a more homogeneous way, and to apply to this clusters a suitable statistic technique that permit us to discover which are the variables that more influence the passengers¿ overall evaluation about the service. The comparison among the results of each cluster will show considerable differences among them and also with the results obtained using the global sample.This study is sponsored by the Consejería de Innovación, Ciencia y Economía of the Junta de Andalucía (Spain) through the Excellence Research Project denominated Q-METROBUS-Quality of service indicator for METROpolitan public BUS transport services . The authors also acknowledge the Granada Consorcio de Transportes for making the data set available for this study. Likewise, Griselda López wishes to express her acknowledgement to the regional ministry of Economy, Innovation and Science of the regional government of Andalusia (Spain) for their scholarship to train teachers and researchers in Deficit AreasDe Oña, R.; López-Maldonado, G.; Díez De Los Ríos, F.; De Oña, J. (2014). Cluster Analysis for Diminishing Heterogeneous Opinions of Service Quality Public Transport Passengers. Procedia - Social and Behavioral Sciences. 162:459-466. https://doi.org/10.1016/j.sbspro.2014.12.227S45946616

Transit service quality analysis using cluster analysis and decision trees: a step forward to personalized marketing in public transportation

[EN] A transit service quality study based on cluster analysis was performed to extract detailed customer profiles sharing similar appraisals concerning the service. This approach made it possible to detect specific requirements and needs regarding the quality of service and to personalize the marketing strategy. Data from various customer satisfaction surveys conducted by the Transport Consortium of Granada (Spain) were analyzed to distinguish these groups; a decision tree methodology was used to identify the most important service quality attributes influencing passengers overall evaluations. Cluster analysis identified four groups of passengers. Comparisons using decision trees among the overall sample of all users and the different groups of passengers identified by cluster analysis led to the discovery of differences in the key attributes encompassed by perceived quality.The authors also acknowledge the Granada Consorcio de Transportes for making the data set available for this study. Griselda Lopez wishes to express her acknowledgement to the regional ministry of Economy, Innovation and Science of the regional government of Andalusia (Spain) for their scholarship to train teachers and researchers in Deficit Areas. Rocio de Ona wishes to express her acknowledgement to the regional ministry of Economy, Innovation and Science of the regional government of Andalusia (Spain) for the Excellence Research Project denominated "Q-METROBUS-Quality of service indicator for METROpolitan public BUS transport services'', co-funded with Feder.De Oña, J.; De Oña, R.; López-Maldonado, G. (2015). 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WEATHER IMPACT ON ROAD ACCIDENT SEVERITY IN MARYLAND

This study was conducted to analyze and quantify the impact of weather factors on road accident severity, based on Maryland accident data during 2007-2010. In order to find a better model fitted related variables, three candidate models multinomial logit (MNL), ordered probit logit (OP), and neural networks were chosen to examine in SAS. The results showed that the Multilayer Perceptron Model in neural networks performed the best and is the accident severity model of choice. During the model construction, eight factors related to weather condition were considered. They were: air temperature, average wind speed, total precipitation in the past 24 hours, visibility, slight, moderate, heavy precipitation and relative humidity. Based on the comparison criteria, we concluded that MNL regression is more interpretive than OP and Neural Networks models. All factors except visibility and heavy precipitation had significant impact on accident severity when considering the data from the entire Maryland highway system. Using MNL, a data subset with accident records only in a section of US route 50 was examined. After excluding the impact factors other than weather, a narrow significant variable set was obtained

Makroskobik ulaşım modelleme teknikleri kullanarak karayollarında trafik güvenliği analizleri

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Bu çalışmada makroskobik ulaşım modelleme teknikleri kullanarak karayollarında trafik güvenliği analizleri yapılmıştır. Bu amaçla, Sakarya Emniyet Müdürlüğünden alınan kaza tutanakları bir ulaşım planlama yazılımı olan Visum Safety yazılımı kullanılarak incelenmiştir. Kaza tutanaklarından elde edilen kaza koordinatları Visum Safety programına aktarılmış, analizler yapılmış ve kazaların yoğun yaşandığı bölgeler kara nokta olarak tanımlanmıştır. Bu kara noktalarda meydana gelen kazalar kazanın oluşumuna, yol durumuna, hava durumuna ve eğitim durumuna göre kategorilendirilip analizi yapılmıştır. Yapılan analizlere göre en çok yaşanan kazalar yayaya çarpma olarak tespit edilmiştir. Kazalar üzerinde eğitim durumunun etkisi ve yeterli trafik deneyiminin olmaması gibi hususlarda tespit edilmiştir. Ayrıca, kötü hava koşullarında sürücülerin daha dikkatli davrandığı ve kazaların daha çok iyi hava koşullarında meydana geldiği de gözlemlenmiştir.In this study, traffic safety analyzes were carried out by using macroscopic transportation modeling techniques. For this purpose, the accident reports obtained from Sakarya Police Department were analyzed by using Visum Safety program which is a transportation planning program. The coordinates of the traffic accidents were transffered into Visum Safety program and the high accident potential spots were defined as the accident black spots. The accients at these black spots are categorized according to the accident formation, road condition, and weather condition and the driver educational status. According to the analyses, the most common accidents occurred in the form of a pedestrian collision. The effect of the educational situation and the lack of sufficient traffic experience were observed and determined on the accidents. Also, it was found that the drivers are more careful during the bad weather conditions than the good weather conditions

DO TRANSPORTATION NETWORK COMPANIES AFFECT ROAD SAFETY OUTCOMES: A SPATIALLY DETAILED ANALYSIS IN SAN FRANCISCO

US traffic fatal deaths have steadily risen since 2010, with the past few years witnessing an unusual trend increase. To reverse such a dangerous trend, one must understand how and why road crashes occur and which factors are causing them. Emerging transportation technologies have shown the potential to improve mobility and safety. However, such technologies are not inherently beneficial and could worsen road safety if not effectively implemented. One such transportation technology that warrants investigation is the rise of ridesharing services, also called Transportation Network Companies (TNCs). The primary goal of the dissertation is to explore the statistical relationship between road safety outcomes and TNC service components like curbside pick-ups and drop-offs (PUDO) or through the TNC-involved vehicles miles traveled (VMT). It evaluates the relationship between TNC service components like PUDO and Tot TNC VMT with four main types of road crash frequency: the total number of road crashes, fatal and severe injury crashes, crashes involving pedestrians and bicyclists, and crashes involving drink-driving using San Francisco (SF) county data. A fixed-effect Poisson Regression Model with a robust covariance matrix compares San Francisco (SF) county\u27s 2010 safety outcomes when TNCs were negligible to safety outcomes for the exact locations in 2016 for which spatially detailed TNC data is available. Dependent variables like Total Crashes, Fatal and Injury Crashes, Pedestrian and Bicyclist Crashes, Alcohol-involving (DUI) Crashes, and Property Damage Only (PDO) Crashes are evaluated using the model, controlling for vehicle speed, Total VMT, and TNC service components, namely TNC VMT and PUDO. We apply that model to 2010 and 2016 scenarios and counterfactual scenarios that estimate what would have occurred in 2016 without specific aspects of TNC operations. The results show that TNCs indirectly increased total crashes by 4% due to higher exposure and 7% due to changes in vehicle speeds. The direct effect of TNCs on crashes offsets these increases, reducing crashes by 14%, but this effect depends upon the model specification and is insignificant in other specifications tested. The results for other types of crashes are similar in direction but lower in significance. Overall, the results suggest that TNCs are a minor factor in road safety outcomes, at least within the limits of what we can measure with the available data. This finding is broadly consistent with past research on the topic. These results interest engineers, planners, and policymakers seeking to improve road safety. Those aiming to reduce traffic crashes would be well-advised to avoid getting distracted by TNCs in one direction or another and instead focus on known solutions, including road design, vehicle technology, and reducing exposure through reducing vehicle miles traveled

Detecting motor vehicle crash blackspots based on their underlying behavioural, engineering, and spatial causes

The state of the practice in crash blackspot identification (BSI) has largely been driven by empirical research without much explicit attention paid to the underlying theoretical assumptions. These embedded assumptions have shaped the science of blackspot identification methodologies and developments over time. Despite the fairly extensive methodological enhancements made during the past five decades, little attention has been paid to reviewing, questioning and possibly revising these underlying theoretical assumptions. The theoretical assumptions underlying blackspot identification include: 1) crash risk can be adequately captured by the total number of crashes at a site divided by the amount of sites’ exposure to potential crashes, 2) designed roads pose differential crash risk to motorists arising from observed (operational) features of the transport network, and 3) crashes are the outcomes of a single source of risk at a site. This doctoral dissertation first reviews the theoretical assumptions underlying blackspot identification, raises fundamental questions about these theoretical assumptions and presents the associated gaps in the blackspot identification literature. These gaps include: 1) non-operational crash contributing factors and their unobserved effects have not been explicitly incorporated into the BSI, and 2) crashes may not be the outcomes of a single source of risk, but rather may be the outcomes of multiple sources of risk at a site. This focus on the underlying theory evolution, its influence on empirical work, and its reflection on remaining theory gaps serves as one of the unique contributions of this research to the literature. A more accurate underlying mechanism for explaining motor vehicle crash causation is then hypothesized as a potential solution to address the research gaps. Stated succinctly, the current theoretical assumption underlying BSI is that crashes are well-approximated by a single source of risk, wherein several contributing factors exert their collective, non-independent influences on the occurrence of crashes via a linear predictor. This PhD study first postulates, and then demonstrates empirically, that crash occurrence may be more complex than can be adequately captured by a single source of risk. It is hypothesized that the total observed crash count at a transport network location is generated by multiple underlying, simultaneous and inter-dependent sources of risk, rather than one. Each of these sources may uniquely contribute to the total observed crash count. For instance, a site’s crash occurrence may be dominated by contributions from driver behaviour issues (e.g. speeding, impaired driving), while another site’s crashes might arise predominately from design and operational deficiencies such as deteriorating pavements and worn lane markings. A multiple risk source methodology is developed to correspond with and empirically test this hypothesis. Two modelling approaches are then used to show the applicability of the multiple risk source methodology: 1) Bayesian latent mixture model, and 2) joint econometric model with random parameters and instrumental variables. Finally, the severity of crashes is explicitly incorporated into the multiple risk source methodology by extending the multiple risk source model to a joint model of crash count and crash severity. To test the viability of the methodological framework, all models are applied to a comprehensive dataset for the state controlled roads in Queensland, Australia and the results are compared with the traditional approaches. The results show that the new multiple risk source models outperform the traditional single risk source models in terms of prediction performance and goodness of fit. In addition, the multiple risk source models are able to provide more insight into crash contributing factors, their impact on the total crash count and their impact on the crash count proportions generated by each risk source. It is found that the parameters of the joint model of crash count and crash severity are moderated by the correlation between these two models and therefore, the total risk at a site can be adequately recognized by crash count and severity, simultaneously. Over all, the findings of this research indicate that decomposing the total crash count into its constituent components, separating the risk sources and incorporating crash severity into the overall framework leads to efficient, cost-effective identification of crash blackspots

Investigating the Effects of Sample Size, Model Misspecification, and Underreporting in Crash Data on Three Commonly Used Traffic Crash Severity Models

Numerous studies have documented the application of crash severity models to explore the relationship between crash severity and its contributing factors. These studies have shown that a large amount of work was conducted on this topic and usually focused on different types of models. However, only a limited amount of research has compared the performance of different crash severity models. Additionally, three major issues related to the modeling process for crash severity analysis have not been sufficiently explored: sample size, model misspecification and underreporting in crash data. Therefore, in this research, three commonly used traffic crash severity models: multinomial logit model (MNL), ordered probit model (OP) and mixed logit model (ML) were studied in terms of the effects of sample size, model misspecification and underreporting in crash data, via a Monte-Carlo approach using simulated and observed crash data. The results of sample size effects on the three models are consistent with prior expectations in that small sample sizes significantly affect the development of crash severity models, no matter which model type is used. Furthermore, among the three models, the ML model was found to require the largest sample size, while the OP model required the lowest sample size. The sample size requirement for the MNL model is intermediate to the other two models. In addition, when the sample size is sufficient, the results of model misspecification analysis lead to the following suggestions: in order to decrease the bias and variability of estimated parameters, logit models should be selected over probit models. Meanwhile, it was suggested to select more general and flexible model such as those allowing randomness in the parameters, i.e., the ML model. Another important finding was that the analysis of the underreported data for the three models showed that none of the three models was immune to this underreporting issue. In order to minimize the bias and reduce the variability of the model, fatal crashes should be set as the baseline severity for the MNL and ML models while, for the OP models, the rank for the crash severity should be set from fatal to property-damage-only (PDO) in a descending order. Furthermore, when the full or partial information about the unreported rates for each severity level is known, treating crash data as outcome-based samples in model estimation, via the Weighted Exogenous Sample Maximum Likelihood Estimator (WESMLE), dramatically improve the estimation for all three models compared to the result produced from the Maximum Likelihood estimator (MLE)