Juan de Oña

Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks

Several different factors contribute to injury severity in traffic accidents, such as driver characteristics, highway characteristics, vehicle characteristics, accidents characteristics, and atmospheric factors. This paper shows the possibility of using Bayesian Networks (BNs) to classify traffic accidents according to their injury severity. BNs are capable of making predictions without the need for pre assumptions and are used to make graphic representations of complex systems with interrelated components. This paper presents an analysis of 1536 accidents on rural highways in Spain, where 18 variables representing the aforementioned contributing factors were used to build 3 different BNs that classified the severity of accidents into slightly injured and killed or severely injured. The variables that best identify the factors that are associated with a killed or seriously injured accident (accident type, driver age, lighting and number of injuries) were identified by inference.

Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks

One of the principal objectives of traffic accident analyses is to identify key factors that affect the severity of an accident. However, with the presence of heterogeneity in the raw data used, the analysis of traffic accidents becomes difficult. In this paper, Latent Class Cluster (LCC) is used as a preliminary tool for segmentation of 3229 accidents on rural highways in Granada (Spain) between 2005 and 2008. Next, Bayesian Networks (BNs) are used to identify the main factors involved in accident severity for both, the entire database (EDB) and the clusters previously obtained by LCC. The results of these cluster-based analyses are compared with the results of a full-data analysis. The results show that the combined use of both techniques is very interesting as it reveals further information that would not have been obtained without prior segmentation of the data. BN inference is used to obtain the variables that best identify accidents with killed or seriously injured. Accident type and sight distance have been identify in all the cases analysed; other variables such as time, occupant involved or age are identified in EDB and only in one cluster; whereas variables vehicles involved, number of injuries, atmospheric factors, pavement markings and pavement width are identified only in one cluster.

A classification tree approach to identify key factors of transit service quality

A key aspect to take into consideration when developing indices to evaluate transit service quality is to determine how much weight passengers give to each attribute when making a global assessment of service quality (SQ). The simplest method of a direct question in customer satisfaction survey (CSS) poses a number of problems, and therefore statistical regression methods have been developed to infer attribute importance on the basis of CSS or stated preference surveys. However, most regression models have their own model assumptions and pre-defined underlying relationships between dependant and independent variables. If these assumptions are violated, the model could lead to erroneous estimations. This paper proposes using a classification and regression tree (CART) that does not require any pre-defined underlying relationship between dependent and independents variables, to identify the key factors affecting bus transit quality of service. The paper uses the data gathered in a CSS conducted on the Granada metropolitan transit system in 2007, which was a non-research oriented survey. Two CART models were developed to compare the key attributes identified before and after making passengers reflect on the main aspects of the system. The outcomes show that, in a preliminary evaluation, passenger perception of SQ is basically influenced by frequency. After being asked to evaluate all the attributes, however, other attributes (e.g. proximity, speed and safety) become more important than frequency.

Quality of Service in Public Transport Based on Customer Satisfaction Surveys: A Review and Assessment of Methodological Approaches

The growth of literature in the field of quality of service in the public transport PT sector shows increasing concern for a better understanding of the factors affecting service quality SQ in PT organizations and companies. A large variety of approaches to SQ have been developed in recent years owing to the complexity of the concept; the broad range of attributes required to evaluate SQ; and the imprecision, subjectivity, and heterogeneous nature of the data used to analyze it. Most of these approaches are based on customer satisfaction surveys. This paper seeks to summarize the evolution of research and current thinking as it relates to the different methodological approaches for SQ evaluation in the PT sector over the years and to provide a discussion of future directions.

Analysis of traffic accident severity using Decision Rules via Decision Trees

A Decision Tree (DT) is a potential method for studying traffic accident severity. One of its main advantages is that Decision Rules (DRs) can be extracted from its structure. And these DRs can be used to identify safety problems and establish certain measures of performance. However, when only one DT is used, rule extraction is limited to the structure of that DT and some important relationships between variables cannot be extracted. This paper presents a more effective method for extracting rules from DTs. The methods effectiveness when applied to a particular traffic accident dataset is shown. Specifically, our study focuses on traffic accident data from rural roads in Granada (Spain) from 2003 to 2009 (both included). The results show that we can obtain more than 70 relevant rules from our data using the new method, whereas with only one DT we would have extracted only five relevant rules from the same dataset.

Extracting decision rules from police accident reports through decision trees

Given the current number of road accidents, the aim of many road safety analysts is to identify the main factors that contribute to crash severity. To pinpoint those factors, this paper shows an application that applies some of the methods most commonly used to build decision trees (DTs), which have not been applied to the road safety field before. An analysis of accidents on rural highways in the province of Granada (Spain) between 2003 and 2009 (both inclusive) showed that the methods used to build DTs serve our purpose and may even be complementary. Applying these methods has enabled potentially useful decision rules to be extracted that could be used by road safety analysts. For instance, some of the rules may indicate that women, contrary to men, increase their risk of severity under bad lighting conditions. The rules could be used in road safety campaigns to mitigate specific problems. This would enable managers to implement priority actions based on a classification of accidents by types (depending on their severity). However, the primary importance of this proposal is that other databases not used here (i.e. other infrastructure, roads and countries) could be used to identify unconventional problems in a manner easy for road safety managers to understand, as decision rules.

Heterogeneity in Perceptions of Service Quality among Groups of Railway Passengers

The aim of this work is to analyze the different perceptions among groups of users regarding transit service quality. We propose a methodology based on a classification and regression tree (CART) approach, allowing the characteristics that most influence overall service quality to be identified. The methodology is applied by using data regarding rail service in northern Italy, particularly suburban lines connecting different towns of the hinterland of the city of Milan. Passengers expressed their opinions about service characteristics such as safety, cleanliness, comfort, information, and personnel. We found that perceptions about service quality are differentiated among the various groups of users.

Neural networks for analyzing service quality in public transportation

It is essential to take into account the service quality assessment made by the passengers of a public transportation system, as well as the weight or relative importance assigned to each one of the attributes considered, in order to know its strengths and weaknesses. This paper proposes using Artificial Neural Networks (ANN) to analyze the service quality perceived by the passengers of a public transportation system. This technique is characterized by its high capability for prediction and for capturing highly non-lineal intrinsic relations between the study variables without requiring a pre-defined model. First, an ANN model was developed using the data gathered in a Customer Satisfaction Survey conducted on the Granada bus metropolitan transit system in 2007. Next, three different methods were used to determine the relative contribution of the attributes. Finally, a statistical analysis was applied to the outcomes of each method to identify groups of attributes with significant differences in their relative importance. The results show that statistical significant differences exist among several categories of attributes that have a greater or lesser impact on service quality and satisfaction. All the methods agree that Frequency is the most influential attribute in the service quality, and that other attributes such as Speed, Information and Proximity are also important.

Transit passengers’ behavioural intentions: the influence of service quality and customer satisfaction

ABSTRACT Knowing passengers’ behavioural intentions to use transit service can be a useful support for transit managers and marketers who can define the most convenient strategies to satisfy existing passengers and attract new ones. We retain that analysing passengers’ intentions to continue to use transit services in the future together with relevant concepts such as service quality and customer satisfaction is fundamental to understand passengers’ behaviour. For this reason, in this paper we propose a structural equation model for investigating on the relationship among some aspects influencing passengers’ behavioural intentions towards the use of transit services. The light rail transit (LRT) of Seville (Spain) offers the transit service supporting our work. We collected through an ad-hoc survey the opinions of the passengers about the used LRT system and transit system in general, and we propose a methodology to explain how passengers’ opinions influence their intentions to use the LRT again. Among the interesting findings from the model, we observe that behavioural intentions are mostly affected by passengers’ judgements about LRT service quality and their satisfaction with the service. Moreover, not only direct but indirect effects on behavioural intentions are derived, determining an accurate conclusion about the relationships of the other concepts with LRT’ users behavioural intentions.

Analysis of transit quality of service through segmentation and classification tree techniques

Perceptions about the quality of service are very different among public transport (PT) users. Users’ perceptions are heterogeneous for many reasons: the qualitative aspects of PT service, users’ socio-economic characteristics, and the diversity of tastes and attitudes towards PT. By analysing different groups of users who share a common characteristic (e.g. socio-economic or travel behaviour), it is possible to homogenise user opinions about the quality of service. This paper studies quality as perceived by users of the metropolitan transit system of Granada (Spain) through a classification tree technique (classification and regression trees (CART)) based on five market segmentations (gender, age, frequency of use, reason for travelling, and type of ticket). CART is a non-parametric method that has a number of advantages compared to other methods that require a predefined underlying relationship between dependent and independent variables. The study is based on data gathered in several customer satisfact...