• Energy Research
• economics and business close
• IT close
• CA close
• Transport Research close

Some empirical findings are presented on the relationship between urban form and work trip commuting efficiency, drawn from the analysis of 1986 work trip commuting patterns in the greater Toronto area. Work trip commuting efficiency is measured with respect to the average number of vehicle kilometers traveled (VKT) per worker in a given zone. Preliminary findings include VKT per worker increases as one moves away from both the central core of the city and from other high-density employment centers within the region; job-housing balance, per se, shows little impact on commuting VKT; and population density, in and of itself, does not explain variations on commuting VKT once other urban structure variables have been accounted for.

In the province of Quebec, Canada where the electricity is nearly carbon-free, the road transport sector represents 35.6% of all emissions. As such, electric vehicles (EVs) have been proposed as a means to reduce such emissions. However, it is not clear how many conventional vehicles (CVs) would need to change to electric in order to meet the greenhouse gas (GHG) emissions reduction target of reducing 1990 CO2 emissions by 37.5% by 2030 in the province. In fact, various considerations exist such as which vehicles will change and how those vehicles are used. This articleaddresses this issue in the case of Montreal, Canada. First, to create a baseline, direct emissions by all personal vehicles in Montreal in 2018 are calculated using data from the 2018 origin-destination (OD) survey and provincial vehicle registration. Next, five scenarios are studied to calculate the variations in the number of EVs needed in the fleet in order to achieve provincial targets. The most optimistic scenario shows that roughly 49% of the fleet would need to change. The most pessimistic scenario estimates that almost 73% of the fleet would need to be converted to EVs. It can be concluded that the strategy used can have a great impact on how many vehicles need to be replaced in the fleet. However, all simulations show that the necessary replacements are far from negligible. It must surely be coupled with other actions such as reducing veh.km travelled (vkmt) or increasing public transport use.

We propose a solution for Electric Vehicles (EVs) energy management in smart cities, where a deep learning approach is used to enhance the energy consumption of electric vehicles by trajectory and delay predictions. Two Recurrent Neural Networks are adapted and trained on 60 days of urban traffic. The trained networks show precise prediction of trajectory and delay, even for long prediction intervals. An algorithm is designed and applied on well known energy models for traction and air conditioning. We show how it can prevent from a battery exhaustion. Experimental results combining both RNN and energy models demonstrate the efficiency of the proposed solution in terms of route trajectory and delay prediction, enhancing the energy management.

This paper attempts to integrate data from models, traditional surveys and big data in a situation of limited information. The goal is to increase the capacity of transport planners to analyze, forecast, and plan passenger mobility. (Big) data are a precious source of information and substantial effort is necessary to filter, integrate, and convert big data into travel demand estimates. Moreover, data analytics approaches without demand models are limited because they allow: (a) the analysis of historical and/or real-time transport system configurations, and (b) the forecasting of transport system configurations in ordinary conditions. Without the support of travel demand models, the mere use of (big) data does not allow the forecasting of mobility patterns. The paper attempts to support traditional methods of transport systems engineering with new data sources from ICTs. By combining traditional data and floating car data (FCD), the proposed framework allows the estimation of travel demand models (e.g., trip generation and destination). The proposed method can be applied in a specific case of an area where FCD are available, and other sources of information are not available. The results of an application of the proposed framework in a sub-regional area (Calabria, southern Italy) are presented.

This paper aims to define an algorithm capable of building the origin-destination matrix from check-in data collected in the extra-urban area of Torino, Italy, where thousands of people commute every day, using smart cards to validate their travel documents while boarding. To this end, the methodological approach relied on a survey over three months to record smart-card validations. Peak and off-peak periods have been defined according to validation frequency. Then, the origin-destination matrix has been estimated using the time interval between two validations to outline the different legs of the journey. Finally, transport demand has been matched with existing bus services, showing which areas were not adequately covered by public transport. The results of this research could assist public transport operators and local authorities in the design of a more suitable transport supply and mobility services in accordance with user needs. Indeed, tailoring public transport to user needs attracts both more customers and latent demand, reducing reliance on cars and making transport more sustainable.

This paper provides a contribution to the international literature by applying regression tree methods to the analysis of the expected effects of the High Speed Rail project in Italy on the tourism market. This approach, as far as the author knows, has never been applied in this context. Tourism and transport information have been gathered for 99 Italian provinces during the 2006–2016 period. Tree-structured methods have been chosen as an application of regression models in which some explanatory variables are used as covariates to predict the dependent variable values on the basis of some decision rules. This approach establishes a casual effect between dependent and independent variables. The dependent variables chosen are the Italian and foreign tourists, and the number of overnights spent by Italians and foreigners. Among the independent variables are the presence of HSR, the presence of first-level airport hubs and the number of operating bases of low-cost airlines; among the attractiveness variables are the GDP, the number of attractions in a given province, the presence of the sea, the population and the percentage of unemployment. The main outcome of this study is that HSR affects the tourism market.