Novel Neural Network Applications to Mode Choice in Transportation: Estimating Value of Travel Time and Modelling Psycho-Attitudinal Factors

Whenever researchers wish to study the behaviour of individuals choosing among a set of alternatives, they usually rely on models based on the random utility theory, which postulates that the single individuals modify their behaviour so that they can maximise of their utility. These models, often identified as discrete choice models (DCMs), usually require the definition of the utilities for each alternative, by first identifying the variables influencing the decisions. Traditionally, DCMs focused on observable variables and treated users as optimizing tools with predetermined needs. However, such an approach is in contrast with the results from studies in social sciences which show that choice behaviour can be influenced by psychological factors such as attitudes and preferences. Recently there have been formulations of DCMs which include latent constructs for capturing the impact of subjective factors. These are called hybrid choice models or integrated choice and latent variable models (ICLV). However, DCMs are not exempt from issues, like, the fact that researchers have to choose the variables to include and their relations to define the utilities. This is probably one of the reasons which has recently lead to an influx of numerous studies using machine learning (ML) methods to study mode choice, in which researchers tried to find alternative methods to analyse travellers’ choice behaviour. A ML algorithm is any generic method that uses the data itself to understand and build a model, improving its performance the more it is allowed to learn. This means they do not require any a priori input or hypotheses on the structure and nature of the relationships between the several variables used as its inputs. ML models are usually considered black-box methods, but whenever researchers felt the need for interpretability of ML results, they tried to find alternative ways to use ML methods, like building them by using some a priori knowledge to induce specific constrains. Some researchers also transformed the outputs of ML algorithms so that they could be interpreted from an economic point of view, or built hybrid ML-DCM models. The object of this thesis is that of investigating the benefits and the disadvantages deriving from adopting either DCMs or ML methods to study the phenomenon of mode choice in transportation. The strongest feature of DCMs is the fact that they produce very precise and descriptive results, allowing for a thorough interpretation of their outputs. On the other hand, ML models offer a substantial benefit by being truly data-driven methods and thus learning most relations from the data itself. As a first contribution, we tested an alternative method for calculating the value of travel time (VTT) through the results of ML algorithms. VTT is a very informative parameter to consider, since the time consumed by individuals whenever they need to travel normally represents an undesirable factor, thus they are usually willing to exchange their money to reduce travel times. The method proposed is independent from the mode-choice functions, so it can be applied to econometric models and ML methods equally, if they allow the estimation of individual level probabilities. Another contribution of this thesis is a neural network (NN) for the estimation of choice models with latent variables as an alternative to DCMs. This issue arose from wanting to include in ML models not only level of service variables of the alternatives, and socio-economic attributes of the individuals, but also psycho-attitudinal indicators, to better describe the influence of psychological factors on choice behaviour. The results were estimated by using two different datasets. Since NN results are dependent on the values of their hyper-parameters and on their initialization, several NNs were estimated by using different hyper-parameters to find the optimal values, which were used to verify the stability of the results with different initializations

Increase in 20–50 Hz (gamma frequencies) power spectrum and synchronization after chronic vagal nerve stimulation

Objective: Though vagus nerve stimulation (VNS) is an important option in pharmacoresistant epilepsy, its mechanism of action remains unclear. The observation that VNS desynchronised the EEG activity in animals suggested that this mechanism could be involved in VNS antiepileptic effects in humans. Indeed VNS decreases spiking bursts, whereas its effects on the EEG background remain uncertain. The objective of the present study is to investigate how VNS affects local and inter regional syncronization in different frequencies in pharmacoresistent partial epilepsy. Methods: Digital recordings acquired in 11 epileptic subjects 1 year and 1 week before VNS surgery were compared with that obtained 1 month and 1 year after VNS activation. Power spectrum and synchronization were then analyzed and compared with an epileptic group of 10 patients treated with AEDs only and with 9 non-epileptic patients. Results: VNS decreases the synchronization of theta frequencies (P!0.01), whereas it increases gamma power spectrum and synchronization (!0.001 and 0.01, respectively). Conclusions: The reduction of theta frequencies and the increase in power spectrum and synchronization of gamma bands can be related to VNS anticonvulsant mechanism. In addition, gamma modulation could also play a seizure-independent role in improving attentional performances. Significance: These results suggest that some antiepileptic mechanisms affected by VNS can be modulated by or be the reflection of EEG changes.2026-2036Pubblicat

Beep4Me: Automatic Ticket Validation to Support Fare Clearing and Service Planning

An integrated transport service fare system, supported by an agreement for ticket revenue sharing among service providers, is an essential component to improve the experience of the users who can find single tickets for the integrated transport services they look for. A challenge is to find a model to share the revenue which all providers agree on. A solution is to adopt data-driven approaches where user-generated data are collected to extract information on the extent each transport service was used. This is consistently used. However, it suffers from incomplete data, as not all users always validate their ticket when checking out or when switching lines. We studied all technologies available to support automatic ticket validation in order to record when the users access and exit each service line. The contributions of this work are the following: we give an in-depth description of the inner workings of this novel approach describing how we take advantage of each technology; we present the developed solution (Beep4Me), which adds new functionalities to an existing mobile ticketing platform; and we describe our testing framework, which includes most cases users might encounter during a trip. Our results demonstrate how it is possible to collect key data related to validations which can be used first for clearing purposes and then for network planning/fleet optimization

The Interplay Between the Choices to Cycle to Work, for Shopping and for Leisure

Recently there has been a surge of interest in cycling as a transport mode able to bring benefits at both the individual and community level, such as enhanced attractiveness of urban areas, low access cost and reduced parking space. An aspect often overlooked in transportation research is how sociodemographic, territorial and psycho-attitudinal factors influence cycling for different purposes. Much of the research has focused on cycling for all purposes, mixing utilitarian and recreational trips, but the determinants (both objective and subjective) triggering the choice to travel by bike may be different, depending on the reason people cycle. To better understand the interplay between the use of the bike for different purposes, we conduct a multivariate analysis. In particular, a series of sub-models are jointly estimated - a multinomial logit that models the commute mode choice, a binary logit that models the choice to use the bike for shopping and a binary logit that models the choice to use the bike for leisure, a structural equation model for the psycho-attitudinal variables. The data used in this study are drawn from a survey conducted in two mid-size urban areas in Italy. A sample of 1,105 individuals with prerequisites useful for the study at hand were used in our analyses. Many insightful results emerged from our analysis. Some socio-economic variables were found to have a significant effect. Males exhibit a greater propensity to cycle both for utilitarian and recreational purposes. Individuals with children are associated with a lower propensity to cycle for commuting, shopping and leisure. Interestingly, living in urban areas, positively influences the propensity to cycle for leisure, presumably because of the existence of better biking facilities as well as the presence in the vicinity of recreation activity locations. Regarding subjective variables (perception of bicycle benefits, perception of cycling comfort and perceived importance of bicycle infrastructure), they were found to positively influence the choice to cycle, for commuting, shopping and leisure, suggesting the importance to consider psychological factors to evaluate individuals’ propensity to cycle. These results are very interesting from a policy point of view. In fact, they strengthen the idea that the implementation of awareness campaigns and educational programmes improves peoples’ perceptions of the bike mode

Does the joint implementation of hard and soft transportation policies lead to travel behavior change? An experimental analysis

Over the last few decades, there has been a growing interest in a variety of travel demand management strategies, both hard and soft, aimed at persuading people to reduce their car use. However, only few studies employed predictive models to assess the effectiveness of soft interventions and understand the impact of both objective and socio-psychological variables on changes in travel behavior. Additionally, though a combination of hard and soft measures is recognized as achieving the best results in reducing car use, few studies differentiate between the effects of the two types. The aim of this work is to quantify the effect of a combination of hard (introduction of a new light railway line) and soft measures (Personalized Travel Plan program) among a group of car drivers in the metropolitan area of Cagliari (Sardinia, Italy). We used data collected before and after the implementation of a Personalized Travel Plan program, where a control group was identified to disentangle the effect of the hard from the soft measure. We specified and estimated an Integrated Choice and Laten Variable (ICLV) model to assess the effect of both objective characteristics and some socio-psychological variables on the choice to use a new light railway service or not. Model results point out that people who lived along the light rail corridor and received and read their Personalized Travel Plan were more likely to switch from car to the light rail. Furthermore, we found that the parameters associated with the psycho-social variables Attachment to the car and Dislike of public transport have a negative influence on the probability to use the new travel alternative. At the same time, our findings on the effect of the soft measure need to be interpreted with some caution as its impact on choice probability was mitigated by travel distance and psycho-social variables

Integrated Choice and Latent Variable model to evaluate the joint effectiveness of the introduction of a new light rail line and an informative measure

There is an ample documentation demonstrating how, during the last 30 years, the use of the car has reached an ever higher level of popularity for many different reasons, like personal convenience and comfort. Nevertheless, car use also generates various negative externalities, like air pollution, noise and personal injuries, whose abatement represents a challenge for every transportation planner. This has therefore led to a growing interest in implementing a variety of policies, both structural and behavioural, to attempt persuading people to reduce the use of their car, by shifting their travel behaviour towards a more sustainable one. However, one of the problems of behavioural interventions measures is the difficulty of properly quantifying their effectiveness, not only by using descriptive analysis, but also making use of predictive models that consider both objective and variables. The aim of this work is that of evaluating the effects of a combination of hard (introduction of a new light railway line) and soft measures (personalized travel plans, PTP) in the metropolitan area of Cagliari (Italy), using control groups and data collected before and after the implementation of the program. In particular, the data was gathered during the progress of the project called “Cittadella Mobility Styles”, which focused on the travel patterns of individuals going to (or away from) a university’s medical-scientific complex (“Cittadella Universitaria”). The final aim of the project was the promotion of the newly built light rail line as a sustainable travel alternative. The data of a total of 194 people was included in the sample analysed. In doing so, we estimate an Integrated Choice and Latent Variable (ICLV) model (Vij and Walker, 2016) to assess the effects of the characteristics of built environment, of demographic indicators and of some psycho-attitudinal variables, on the choice of switching to the new light railway. To the best of the authors’ knowledge, this should be the first example of ICLV model that considers all the aforementioned elements. To evaluate the effects of the soft measure, three dummy variables were considered. The first one refers to individuals who received a PTP; the second one is specific for the control group; the last one is instead used to indicate car users who did not belong in the previous categories. The modelling results show instead that the latent variables “aversion to public transport” and “attachment to the car” negatively influence the propensity to change travel behaviour. Another aspect that emerges is that the likelihood to change travel behavior strongly depends on the type of intervention, with people people who received the PTP more likely to change than people belonging to the control group. REFERENCES Vij, A., & Walker, J. L. (2016). How, when and why integrated choice and latent variable models are latently useful. Transportation Research Part B: Methodological, 90, 192-217