Analisi comportamentale della scelta del percorso attraverso l'utilizzo di nuove tecnologie di acquisizione delle informazioni

摘要

In travel demand modeling, route choice is one of the most complex decision-makingudcontexts to understand and mathematically represent for several reasons. Firstly, a largeudnumber of available paths may exist between the same origin-destination (OD) pair.udSecondly, neither the traveler nor the modeler are aware of all the available alternatives.udThirdly, individual choices are dictated by different constraints and preferences that areuddifficult to capture by modelers who face increasingly larger datasets where retrieving theudexact path chosen by travelers is not always straightforward. Last, there is a lot ofuduncertainty about travelers’ perceptions of route characteristics as well as otherudcharacteristics that can influence their choices, such as age, gender, habit, weatherudconditions and network conditions. This highlights the difficulties encountered forudinterpreting individual user behavior in greater depth. The rapid advances in GPS devices,udhas resulted in major benefits for data collection, which now can be recorded automaticallyudand with greater accuracy compared to the techniques used in the past (phone calls, e-mails,udface-to-face interviews, laboratory experiments.).udOn these basis the main objective of the thesis is then to study route choice using a GPSuddatabase. The data were acquired during a survey, named "Casteddu Mobility Styles” (CMS),udconducted by the University of Cagliari (Italy) in the metropolitan area of Cagliari betweenudFebruary 2011 and June 2012. Each participant was asked to carry a smartphone with builtinudGPS in which an application called “Activity Locator” – implemented by CRiMM (Centre forudResearch on Mobility and Modeling) – had been installed. A total of 8831 trips were recordedudby 109 individuals, of which 4791 referring to the car driver mode. Each GPS trackud(consisting of a sequence of referenced position points) was then treated with map-matchingudtechniques, through which it was possible to associate each “GPS point” to a link of theudnetwork, thus creating the observed route database.udThe first objective of the thesis is to understand which are the characteristics of the dataudacquired during the CMS survey, doing firstly the same analysis that other authors did inudtheir researches based on GPS data. In almost all the previous researches, the GPS data wereudcollected through in-vehicle surveys that make it possible to gather objective information onudtrips (travel times and distances). Pre-and post-analysis interviews were conducted toudgather information about the subjective characteristics of the individuals and GIS platformsudwere used to study the routes. In the present study, the data were collected using anudintegrated system able to also record the activities conducted, along with all theudcharacteristics associated thereto. In this way a complete database was created containing alludthe information (objective and not) concerning the trips. For comparisons with theudobjectively most convenient paths, then, was used a static macrosimulation modelud(implemented in CUBE, Citilabs Ltd.) of the entire study area (Cagliari and its metropolitan area), which reproduces the network characteristics actually encountered by the driversudreferring to the data used.udFrom this first analysis it was observed that when more than one route is taken for repetitiveudtrips between the same OD. In order to understand these particular behavior of users, namedudalso intravariability, discrete choice models were estimated. It’s important to note that in theudprevious GPS-based researches this particular behavior was only identified, withoutudstudying it in depth. Several other studies, focused on route switching behavior, tried toudunderstand it applying discrete choice models, but their database were based on dataudacquired through questionnaires or laboratory experiments, and for the majority the routeudswitching behavior was studied in relation to the trip information provision. The objective ofudthis analysis is then to combine the two fields of the research on route switching, trying toudunderstand it estimating discrete choice models using a GPS based database, closing the gapudof the previous researches. The final goal of the model estimations is to understand whichudare the main attributes of the routes and the characteristics of the users that most influenceudthe choice of an habitual route for the same origin-destination (OD) trip.udAfter these first analysis, the final objective of the thesis is to apply a route choice model toudGPS-based data. Modeling route choice behavior is generally framed as a two-stage process:udgeneration of the alternative routes and modeling of the choice from the generated choiceudset. The focus of this step of the research is on the bias that might be introduced in the modeludestimation by the choice set generation process. Specifically, although several explicit choiceudset generation techniques are found in the literature, the focus is on stochastic routeudgeneration and the correction for unequal sampling probability of routes when applying thisudtechnique that is easily applicable to large-scale networks. Indeed, stochastic routeudgeneration is a case of importance sampling where the selection of the path depends on itsudown properties, so route choice models based on stochastic route generation must include audsampling correction coefficient that accounts for the different selection probability. In thisudstudy is proposed a methodology for calculating and considering this correction factor intoudMNL-based models with choice sets generated by means of stochastic route generation.udSpecifically, was decided to look at the sampling correction factor proposed for the randomudwalk algorithm and to calculate the route selection probability in order to exploit thisudexpression. Therefore, a procedure is proposed for the computation of the selectionudprobabilities on the basis of the stochastic generation principle, then the correction factorudand last the EPS for model estimation. The modeling analysis confirms the functionality ofudthe proposed approach that has great advantages: (i) it provides insight into the applicationudof stochastic generation in route choice modeling, especially in large-scale networks whereudthe only need is a standard random number generator and a Dijkstra algorithm; itudproposes a simple and manageable procedure from the computational perspective for the calculation of route selection probabilities and hence the correction factor and EPS for modeludestimation; it proves the efficiency of the proposed methodology on revealed preferenceuddata in a dense urban network by showing an increase in goodness-of-fit of the model and audshift from illogical to logical sign in parameters estimated for key variables such as travel time.