During the last two decades, the development of sustainable transportation systems has been highlighted as a key element in solving environmental problems related to climate change and impacts on greenhouse gases. Globally, the transportation sector has become one of the main contributors to these environmental problems. Thus, the environmental impact assessment of this sector and the implications of new vehicle technologies have begun to be considered as first steps for any long-term future strategies in this sector. In Palestine, the lack of environmental data related to the transportation sector and the absence of studies that address the new vehicle technologies (such as hybrid vehicles) and their future implications make it difficult to set up any future strategies or plans. In this study, the current and the future environmental impacts of the transportation sector have been assessed, and the future implications of hybrid vehicles have been determined. The gross domestic product (GDP), population, and the number of vehicles for the period 1994–2018 have been used to develop an auto regressive integrated moving average (ARIMA) prediction model for the future number of vehicles. Then, the total traveled kilometers and the total consumed fuels (by diesel and gasoline vehicles) have been predicted. After that, the current and future (2020 and 2030) greenhouse gas (GHG) emissions, including CO₂, N₂O, and CH₄, have been estimated. Finally, the future implications of hybrid vehicles, based on two scenarios (10% and 20% hybrid vehicles) have been estimated. The results have showed that the estimated CO₂, N₂O, and CH₄ emissions from the transportation sector in 2020 are 4,842,164.5, 213.8, and 445.8 tons, which are very high, and even much higher than the total national emissions of 2014 (the only officially available data). Moreover, in 2030, replacing 20% of internal combustion engine vehicles (ICEVs) by hybrid vehicles would lead to 4.66% and 13.31% reductions in CO₂ and N₂O, respectively, as compared to 100% ICEVs, while the CH₄ emissions will increase. However, the overall CO₂-equivalent will decrease by 5%; therefore, a more sustainable transport system will be achieved.

Recently, developing sustainable public transportation systems has been highlighted by decision makers and transportation agencies, due to the development of urban areas and the related environmental problems. Implementing new vehicle technologies has been introduced as an appropriate alternative to the conventional taxis. Hybrid electrical vehicles (HEVs) have been the potential candidates for replacing the conventional taxis, since they are more eco-friendly than conventional ones and even more reliable than electric vehicles (EVs) as a mode of public transportation. In this study, current and future environmental impact assessments have been determined for the taxi fleet in the West Bank, Palestine, and the implications of using new vehicle technologies (hybrid taxis) as a replacement of the conventional taxi fleet have been investigated. In order to perform this study, firstly, the data of the number of taxis for the period of 1994–2018 have been collected and a prediction model for the future number of taxis has been developed. The expected total amounts of consumed fuels have been then estimated. Finally, the current and the future N₂O and CO₂, and emissions, have been estimated and the expected influences of hybrid taxis have been determined. The results of the analysis have concluded that replacing 50% of conventional taxis with a hybrid fleet could achieve 42.3% and 28% reductions in N₂O and CO₂, respectively, in the next 10 years. A 395% increase in CH₄ could be obtained due to the higher amount of CH₄ that is produced by the gasoline combustion compared to the diesel fuel, since hybrid vehicles have gasoline-based engines (GHG in terms of CO₂-equivalent could be increased by 28.2%).

During the last two decades, problems related to high-energy consumption and greenhouse gas (GHG) emissions by the transportation sector have arisen. Therefore, several alternatives have been investigated, in order to reduce the dependence on the conventional fuels that are used by land transportation modes. One of these promising alternatives is the electric vehicles (EVs), which seem to be the future replacement for conventional vehicles. Thus, this study aims to quantify the energy and the environmental implications of EVs in Palestine in 2030, considering that the current percentage of EVs is almost 0%. In this study, the needed parameters related to the number of vehicles and energy costs by source in Palestine were collected. Then, several prediction models have been developed in order to determine the energy and environmental implications of EVs in 2030, considering that 10% of conventional vehicles could be replaced by EV during the next 10 years (2020–2030). The results have showed that this could save USD 464.31 million in operating energy costs during 2030 (one year of saving). More specifically, this number is almost equal to 3.18% of the gross domestic product (GDP) of Palestine in 2018. Furthermore, significant amounts of GHG emissions could be reduced. The reduction (during 2030) in NH₄, N₂O, and CO₂ emissions could be around 10.51%, 10%, and 6.86%, respectively.

Road crashes are problems facing the transportation sector. Crash data in many countries are available only for the past 10 to 20 years, which makes it difficult to determine whether the data are sufficient to establish reasonable and accurate prediction rates. In this study, the effect of sample size (number of years used to develop a prediction model) on the crash prediction accuracy using Autoregressive integrated moving average (ARIMA) method was investigated using crash data for years 1971–2015. Based on the availability of annual crash records, road crash data for four selected countries (Denmark, Turkey, Germany, and Israel) were used to develop the crash prediction models based on different sample sizes (45, 35, 25, and 15 years). Then, crash data for 2016 and 2017 were used to verify the accuracy of the developed models. Furthermore, crash data for Palestine were used to test the validity of the results. The used data included fatality, injury, and property damage crashes. The results showed similar trends in the models’ prediction accuracy for all four countries when predicting road crashes for year 2016. Decreasing the sample sizes led to less prediction accuracy up to a sample size of 25; then, the accuracy increased for the 15‐year sample size. Whereas there was no specific trend in the prediction accuracy for year 2017, a higher range of prediction error was also obtained. It is concluded that the prediction accuracy would vary based on the varying socioeconomic, traffic safety programs and development conditions of the country over the study years. For countries with steady and stable conditions, modeling using larger sample sizes would yield higher accuracy models with higher prediction capabilities. As for countries with less steady and stable conditions, modeling using smaller sample sizes (15 years, for example) would lead to high accuracy models with good prediction capabilities. Therefore, it is recommended that the socioeconomic and traffic safety program status of the country is considered before selecting the practical minimum sample size that would give an acceptable prediction accuracy, therefore saving efforts and time spent in collecting data (more is not always better). Moreover, based on the data analysis results, long‐term ARIMA prediction models should be used with caution.

Introduction: As fatalities, injuries, and economic losses from road accidents are a major concern for governments and their citizens, Australia, like other countries, has designed and implemented a wide range of strategies to reduce the rate of road accidents. Methods: As part of the strategy design process, data on crash deaths were collected and then analyzed to develop more effective strategies. The data of crash deaths in Australia during the years 1965 to 2018 were analyzed based on gender, causes of crash deaths, and type of road users, and then the results were compared with global averages, then a prediction model was developed to forecast the future annual crash fatalities. Results: The results indicate that, based on gender, the rate of male road fatalities in Australia was significantly higher than that of female road fatalities. Whereas based on the cause of death, the first cause of death was over speeding. Based on the type of road users, the drivers and passengers of 4-wheel vehicles had the highest rate of fatalities. Conclusion: The prediction model was developed based on Autoregressive Integrated Moving Average (ARIMA) methodology, and annual road fatalities in Australia for the next five years 2019-2022 have been forecast using this model.