Cristina Pronello

Bio: Cristina Pronello is an academic researcher from Polytechnic University of Turin. The author has contributed to research in topics: Population & Public transport. The author has an hindex of 16, co-authored 58 publications receiving 752 citations. Previous affiliations of Cristina Pronello include University of Paris & University of Technology of Compiègne.

Latent Variables and Route Choice Behavior

Abstract: In the last decade, a broad array of disciplines has shown a general interest in enhancing discrete choice models by considering the incorporation of psychological factors affecting decision making. This paper provides insight into the comprehension of the determinants of route choice behavior by proposing and estimating a hybrid model that integrates latent variable and route choice models. Data contain information about latent variable indicators and chosen routes of travelers driving regularly from home to work in an urban network. Choice sets include alternative routes generated with a branch and bound algorithm. A hybrid model consists of measurement equations, which relate latent variables to measurement indicators and utilities to choice indicators, and structural equations, which link travelers' observable characteristics to latent variables and explanatory variables to utilities. Estimation results illustrate that considering latent variables (i.e., memory, habit, familiarity, spatial ability, time saving skills) alongside traditional variables (e.g., travel time, distance, congestion level) enriches the comprehension of route choice behavior.

Latent variables and route choice behavior

Abstract: In the last decade, a broad array of disciplines has shown a general interest in enhancing discrete choice models by considering the incorporation of psychological factors affecting decision making. This paper provides insight into the comprehension of the determinants of route choice behavior by proposing and estimating a hybrid model that integrates latent variable and route choice models. Data contain information about latent variable indicators and chosen routes of travelers driving regularly from home to work in an urban network. Choice sets include alternative routes generated with a branch and bound algorithm. A hybrid model consists of measurement equations, which relate latent variables to measurement indicators and utilities to choice indicators, and structural equations, which link travelers' observable characteristics to latent variables and explanatory variables to utilities. Estimation results illustrate that considering latent variables (i.e., memory, habit, familiarity, spatial ability, time saving skills) alongside traditional variables (e.g., travel time, distance, congestion level) enriches the comprehension of route choice behavior.

Relative influence of acceleration and speed on emissions under actual driving conditions.

Abstract: This paper assesses the impact of speed and acceleration on exhaust pollutant emissions. Kinematic sequences were randomly selected from actual driving recordings in order to constitute a representative set of driving conditions. For each sequence, average speed and positive acceleration were calculated. Instantaneous and integrated pollutant emissions were calculated using an emission model, based on instantaneous emission measurements. Emissions of CO, CO2, HC and NOx were analysed for diesel, conventional and catalyst vehicles, and finally the whole French car fleet. A significant influence of speed on emissions was observed. The acceleration impact was relatively low, and much lower than the influence observed when considering instantaneous values, except for the catalyst vehicles, which showed the strongest dependence on acceleration. This low impact on emissions shows that the potential emissions reduction through the improvement of traffic conditions could be relatively limited.

Last mile freight distribution and transport operators’ needs: which targets and challenges?

Abstract: The paper aims at investigating the needs of three sets of stakeholders – retailers and HoReCa, transport operators and local authorities – aimed at designing a dynamic and participatory platform of services and applications for the optimisation of the last mile urban logistics. The research has been carried out in the city of Torino where some integrated services will be introduced: a) management and booking of loading/unloading areas; b) experimental dropbox for collection and delivery; c) the introduction of dynamic access to the limited traffic zone (ZTL, Zona a Traffico Limitato); d) real-time dynamic optimisation of routes. To meet this goal a survey has been administered to a sample of transport operators, points of sale, public administration. The survey has been designed using a quali-quantitative method (web-questionnaire, interviews and focus groups). The results have showed a large gap between the needs of the operators (transport operators and retailers) and the strategies that the administration would seek to implement. A lack of interaction between the stakeholders has led to misperceive the problems that the carriers have to face daily and to overestimate the importance of the technology in solving the last mile issues. Prior to designing complex and expensive platforms, it is fundamental to implement some basic measures, closely related to the efficiency of the unloading and delivery of goods within the urban centre.

Assessing Public Opinions of and Interest in New Vehicle Technologies: An Austin Perspective

Abstract: Technological advances are bringing connected and autonomous vehicles (CAVs) to the ever- evolving transportation system. Anticipating the public acceptance and adoption of these technologies is important. A recent internet-based survey was conducted polling 347 Austinites to understand their opinions on smart-car technologies and strategies. Ordered-probit and other model results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation ($7,253) appears to be much higher than that for adding partial (Level 3) automation ($3,300) to their current vehicles. This study estimates the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding such automations and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios ($1, $2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, and home-location decisions after AVs and SAVs become a common mode of transport. Higher-income, technology-savvy males, living in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.

Handbook Of Practical Program Evaluation

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On-road motor vehicle emissions and fuel consumption in urban driving conditions.

Abstract: This paper reports on the analysis of on-road vehicle speed, emission, and fuel consumption data collected by four instrumented vehicles. Time-, distance-, and fuel-based average fuel consumption, as well as CO, HC, NOx, and soot emission factors, were derived. The influences of instantaneous vehicle speed on emissions and fuel consumption were studied. It was found that the fuel-based emission factors varied much less than the time- and distance-based emission factors as instantaneous speed changed. The trends are similar to the results obtained from laboratory tests. The low driving speed contributed to a significant portion of the total emissions over a trip. Furthermore, the on-road data were analyzed using the modal approach. The four standard driving modes are acceleration, cruising, deceleration, and idling. It was found that the transient driving modes (i.e., acceleration and deceleration) were more polluting than the steady-speed driving modes (i.e., cruising and idling) in terms of g/km and g/sec. These results indicated that the on-road emission measurement is feasible in deriving vehicle emissions and fuel consumption factors in urban driving conditions.