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The development of car fleet plays major part when considering the emissions resulted from passenger car use. Globally, there are many different methods and models for car fleet development estimation, but majority of these represent the results only on national level. In Finland, existing models do not note differences between area types. In this paper, we introduce car fleet model (SALAMA), which can be used to estimate car fleet development for both national and regional levels in Finland. Model allows to alter different variables (such as car fleet development speed, car ownership level and probability to change driving power) to examine, how different policy changes would affect the car fleet development. The paper depicts the Finnish baseline scenario compared to the effects of faster (30% faster than baseline) and slower (30% slower than baseline) fleet development speed scenarios. On the baseline scenario, model estimates 320 000 electric cars (both BEV and PHEV) in 2030 and 830 000 in 2040. For faster scenarios, model estimates 100 000 and 250 000 electric cars more in respective years, while slower scenario produces 80 000 and 180 000 electric cars less compared to baseline scenario. In addition, the model shows that there are clear differences between area types. For example, the share of electric car increases faster in urban areas than in rural areas. • Car fleet development model for Finland is presented. • Model produces results for both national and regional level. • Baseline scenario estimates a total of 830 000 BEV and PHEV in 2040 • Fast scenario estimates a total of 1 100 000 BEV and PHEV in 2040 • Fast fleet development alone will not fulfil 2030 EU emission reduction targets. [ABSTRACT FROM AUTHOR]

The rapid adoption of electric vehicles (EVs) has led to dramatic increase in charging demands that poses great challenges for efficient charging infrastructure rollout and operation. It is crucial to accurately assess charging demand in urban areas to optimize the siting and sizing of charging infrastructure. This paper proposes a novel urban charging load forecasting model for private passenger EVs based on massive operating data of EVs in Beijing. First, the characteristics of travel patterns for private passenger EVs, urban road network, functional area distribution and charging infrastructure distribution within the entire Beijing area are identified. Then a charging load forecasting model that can simultaneously simulate trip chains for EVs is constructed by considering the occupancy states of public charging piles and the interactions among different EVs. Finally, the effectiveness of the proposed charging load forecasting model is verified based on comprehensive test data. Our findings imply that the number of EVs at recharge and the charging power can be reliably predicted with the accuracy of over 84.73 % and 81.92 %, respectively. It provides the foundation for optimal charging infrastructure planning and charging scheduling. [ABSTRACT FROM AUTHOR]

Predicting taxi demands in large cities can help in better traffic management as well as ensure better commuter satisfaction for an intelligent transportation system. However, the traffic demands across different locations have varying spatio-temporal correlations that are difficult to model. Despite the ability of the existing Deep Neural Network (DNN) models to capture the non-linearity in spatial and temporal characteristics of the demand time-series, capturing spatio-temporal characteristics in different real-world scenarios like varying historic and prediction time frame, spatio-temporal variations due to noise or missing data, etc. still remain a big challenge for the state-of-the-art models. In this paper, we introduce En coder- App ro X imator (EnAppX), an encoder–decoder DNN-based model that uses Chebyshev function approximation in the decoding stage for taxi demand times-series prediction and can better estimate the time-series in the presence of large spatio-temporal variations. Opposed to any existing state-of-the-art model, the proposed model approximates complete spatio-temporal characteristics in the frequency domain which in turn enables the model to make a robust and improved prediction in different scenarios. Validation over two real-world taxi datasets from different cities shows a considerable improvement of around 23% in RMSE scores compared to the state-of-the-art baseline model. Unlike several existing state-of-the-art models, EnAppX also produces improved prediction accuracy across two regions for both to and fro demands. [ABSTRACT FROM AUTHOR]

• This paper hybridizes phase space reconstruction and weighted local constant method for short-term load forecasting. • The proposed model has good dynamic characteristics and adaptability; can capture changes in time, and can respond quickly. • Experimental results reveal that the proposed model has better forecasting accuracy than other alternative model. To forecast accurately the electric load of a city at the weekend, aiming at the problems of nonlinearity and model interpretability in power behavior, a local constant first-order weighted forecasting model is developed. First of all, the values of the electric load are reconstructed in phase space, stability analysis can effectively describe the operation mechanism and law of weekend power. Then, the weights of neighbors are used in the first-order local forecasting model, and the nearest neighbors are used in grid optimization. The forecasted values are then separated to be the phase forecasting points in terms of the spatial dimension of the electric loads and the lags of the time delay obtained in the training stage. Numerical experimental results of samples at different scales show the following: (1)The forecasting performance of the proposed model is better than other models in the forecasting errors in this work (among three types of measurements: root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE)); (2) the significance test (KSPA) also confirmed the universality and consistency of the method, and the effective diagnosis and statistical analysis of the weekend power mechanism are helpful to promote weekend power management and commercial design. [ABSTRACT FROM AUTHOR]

Following a federal policy statement in 2010 supporting bicycle and pedestrian accommodation in federal-aid transportation projects, many cities across the US have implemented Complete Streets principles and invested in developing better-planned infrastructure that can be safely accessed by a diversity of modes of transportation by all types of users, in a mix of land uses. However, most of the travel demand forecasting models and planning tools used in practice are not sensitive to changes in demand for non-motorized modes such as walking and cycling in response to road infrastructure improvements. Hence, there is a need for models and tools that are capable of evaluating impacts of infrastructure changes that include Complete Streets implementations on the travel behavior, and estimate shifts in mode choices from motorized to non-motorized modes. This paper proposes a specific data collection plan, a multi-modal choice model, and strategies to update traditional trip-based transportation models to forecast rates of non-motorized trips for evaluating Complete Streets plans at a higher level. Concretely, we estimate elasticities to Level of Traffic Stress, which defines the comfort or discomfort experienced by walkers and bikers, segmented by income levels and trip purposes. We then use them to compute the new non-motorized mode shares that would be achieved by improving CS attributes leading to lower levels of traffic stress. The proposed modeling framework has been successfully applied to the Maryland Statewide Transportation Model, producing reliable non-motorized trip rates, and can be extended to other methodological frameworks used by public agencies. [ABSTRACT FROM AUTHOR]