Martin Trépanier

Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System

The Smart Card Automated Fare Collection (SCAFC) system is an Intelligent Transportation System that is becoming increasingly popular among transit operators. In addition to fare control, the data collected by these systems can be very useful in transit planning. Many SCAFC systems store the location where the passenger boarded due to the positioning device carried onboard; however, in most systems alighting locations are not validated and, thus, not stored in databases. This article presents a model to estimate the destination location for each individual boarding a bus with a smart card. Experiments carried out with a database programming approach show that the data must be thoroughly validated and corrected prior to the estimation process. The first application of the model provided a success rate of 66% for destination estimation, reaching about 80% at peak hours. Further research will tackle the issues of error detection, correction, and link results, comparing them with those of other data sources.

MINING PUBLIC TRANSPORT USER BEHAVIOUR FROM SMART CARD DATA

Abstract In urban public transport, smart card data is made of millions of observations of users boarding vehicles over the network across several days. The issue addresses whether data mining techniques can be used to study user behaviour from these observations. This must be done with the help of transportation planning knowledge. Hence, this paper presents a common “transportation planning/data mining” methodology for user behaviour analysis. Experiments were conducted on data from a Canadian transit authority. This experience demonstrates that a combination of planning knowledge and data mining tool allows producing travel behaviours indicators, mainly regarding regularity and daily patterns, from data issued from operational and management system. Results show that the public transport users of this study can rapidly be divided in four major behavioural groups, whatever type of ticket they use..

How Carsharing Affects the Travel Behavior of Households: A Case Study of Montréal, Canada

ABSTRACT Although carsharing is spreading across the world, few studies have examined its collective benefits. There is a need to analyze the socio-demographic features of users, and assess the role and impact of carsharing on the overall travel behaviors of their households. This article compares the results of two simultaneous surveys in the Montréal area: a web-based survey among carsharing users; and a regional, large-scale household travel survey. Results show a significant difference in modal split of car use between carsharing users and typical residents, for various types of households and levels of carsharing use. In all cases, carsharing members never reached the level of car use observed in the population.

Detection of Activities of Public Transport Users by Analyzing Smart Card Data

During the past decade, a significant amount of research has been dedicated to the use of smart card data for various purposes. A method is presented for the detection and estimation of the location, time, duration, and purpose of activities undertaken by public transit users with the use of smart card databases and other available information about land use and user behavior. The method is applied to cases in Santiago, Chile, and Gatineau, Quebec, Canada, to identify activity purpose and time frame to characterize user behavior. The results obtained for each city are compared to discover differences in behavioral activity patterns due to sociological, cultural, and geopolitical differences.

Analysing the Variability of Transit Users Behaviour with Smart Card Data

This paper proposes various measures regarding the variability of travel behaviours of transit users. The analyses are performed with smart card data collected over a ten months period. The variability in terms of boarding per day and new stops frequented with the days of travel on the transit network is examined. Data mining techniques are then used to classify days of travel according to the similarity of the boarding time periods. In this view, the use of two specific smart cards is examined in more details. These experiments first show that the behaviours of regular transit users evolve with time both in terms of transit stops frequented and time of boarding. Hence, variability of behaviours also changes for various user types

The capacitated arc routing problem with refill points

This article introduces the capacitated arc routing problem with refill points (CARP-RP). The vehicle servicing arcs must be refilled on the spot by using a second vehicle. The problem consists on simultaneously determining the vehicles routes that minimize the total cost. An integer linear programming model is proposed and tested.

Calculation of Transit Performance Measures Using Smartcard Data

This paper illustrates the use of smartcard data to estimate various transit performance measures. Combined with well-established evaluation processes, such measures can help operators monitor their networks in greater detail. The performance of the network supply and the statistics on passenger service can be calculated from these datasets for any spatial or temporal level of resolution, including route and bus stop levels.

Review: A survey of models and algorithms for emergency response logistics in electric distribution systems. Part II: Contingency planning level

This is the second part of a two-part survey of optimization models and solution algorithms for emergency response planning in electric distribution systems. The first part of the survey addresses reliability planning problems with fault considerations related to electric distribution operations. The aim of this second part is to provide a comprehensive survey of optimization models and solution methodologies for contingency planning problems related to electric distribution operations. These problems include the restoration of service, the sequencing of switching operations, the routing of repair vehicles, the scheduling of repair crews, and the assignment of crews to repair sites.

Integration of inventory and transportation decisions in decentralised supply chains

In decentralised supply chains, lack of visibility, long delivery delays and complex transportation networks make it difficult to integrate inventory control with other logistics activities. However, because of the impact of stock turnover on just-in-time operations, inventory control has to be considered in the global optimisation of the supply chain. In the literature, transportation and inventory control decisions are seldom modelled together, because minimising transportation costs and increasing inventory turns are two contradictory objectives. This paper addresses this global optimisation problem. It presents a Decision Support System (DSS) that estimates logistics activities in a decentralised supply chain by integrating inventory control and transportation operations. Delivery frequencies and phases are the decision variables used to study the behaviour of the logistics system.

Review: A survey of models and algorithms for emergency response logistics in electric distribution systems. Part I: Reliability planning with fault considerations

Emergency response operations in electric distribution systems involve a host of decision-making problems at the reliability and contingency planning levels. Those operations include fault diagnosis, fault location, fault isolation, restoration, and repair. As the first of a two-part survey, this paper reviews optimization models and solution methodologies for reliability planning problems with fault considerations related to electric distribution operations. Contingency planning problems of emergency distribution response are discussed in the second part. The present paper surveys research on determining a distribution substation single-fault capacity, reallocating excess load, configuring distribution systems, partitioning a geographical area into service territories, and locating material stores and depots.