This paper proposes an extended Kalman filter for quasi-dynamic estimation/updating of o-d flows from traffic counts. The quasi-dynamic assumption--that is considering constant o-d shares across a reference period, whilst total flows leaving each origin may vary for each sub-period within the reference period--has been proven already realistic and effective in off-line o-d flows estimation using generalized least squares estimators. The specification of the state variables and of the corresponding transition and measurement equations of a quasi-dynamic extended Kalman filter are illustrated, and a closed-form linearization is presented under the assumption of an uncongested network and error-free assignment matrix. Results show satisfactory performance and parsimonious computational burden on real-size networks.

This paper studies the behavior of an energy consumption model for electric vehicles in presence of input uncertainty, that is under heterogeneous driving behaviors and traffic conditions, or at varied performances of technology and operating scenarios. In fact, these sources of uncertainty are often neglected in customary applications, i.e. the model is evaluated only around nominal conditions, thus inhibiting the model's ability to capture the intrinsic variability of energy consumptions observed in real driving. Therefore, a general framework to perform global and regionalized sensitivity analysis on energy consumption models is here formulated and applied to identify which model inputs contribute the most to the variance of simulated energy consumption and recovery, and which input values lead to average or extreme model outputs. Results proved that driving behaviors and traffic flow dynamics have the greatest impact on simulated consumption and show strong interaction effects with the parameters of the regenerative braking strategy with respect to the recovered energy. While the vehicle weight and the auxiliary systems have an increasing impact at lower speeds, the technology performances and the vehicle characteristics were proved to be non-influential at all. Ultimately, the model was validated against its ability to reproduce the variability of consumptions observed in field experiments. Results show a tendency to over-estimating average consumption and underestimating average recovery, i.e. the model predictions are conservative. In addition, evidence shows that there are un-modeled details of the regenerative braking strategy to be further investigated via future experimental analyses.

This paper presents macro-level safety performance functions and aims to provide empirical tools for planners and engineers to conduct proactive analyses, promote more sustainable development patterns, and reduce road crashes. In the past decade, several studies have been conducted for crash modeling at a macro-level, yet in Italy, macro-level safety performance functions have neither been calibrated nor used, until now. Therefore, for Italy to be able to fully benefit from applying these models, it is necessary to calibrate the models to local conditions. Generalized linear modelling techniques were used to fit the models, and a negative binomial distribution error structure was assumed. The study used a sample of 15,254 crashes which occurred in the period of 2009–2011 in Naples, Italy. Four traffic analysis zones (TAZ) levels were used, as one of the aims of this paper is to check the extent to which these zoning levels help in addressing the issue. The models were developed by the stepwise forward procedure using explanatory Socio-Demographic (S-D), Transportation Demand Management (TDM), and Exposure variables. The most significant variables were: children and young people placed in re-education projects, population, population aged 65 and above, population aged 25 to 44, male population, total vehicle kilometers traveled, average congestion level, average speed, number of trips originating in the TAZ, number of trips ending in the TAZ, number of total trips and, number of bus stops served per hour. An important result of the study is that children and young people placed in re-education projects negatively affects the frequency of crashes, i.e., it has a positive safety effect. This demonstrates the effectiveness of education projects, especially on children from disadvantaged neighbourhoods.