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Discrete choice models are commonly used in transportation planning and modeling, but their theoretical basis and applications have been mainly developed in a static context. In this paper, we propose an estimation technique for analyzing the impact of technological changes on the dynamic of consumer demand. The proposed research presents a dynamic formulation that explicitly models market evolution and accounts for consumers’ expectations of future product characteristics. The timing of consumers’ decisions is formulated as a regenerative optimal stopping problem where the agent must decide on the optimal time of purchase. This model frame will be further improved by modeling the choice from a set of differentiated products whose characteristics randomly change over time. The framework proposed is developed and applied in the context of car ownership.

\"This book explores, ethnographically, the entanglement between the rise of the automotive regime and emergence of the middle class in South China, through which a nuanced picture of China's great transformations is depicted\"-- Provided by publisher.

In the U.S., households with less than one car per driver (auto-deficit households) are more than twice as common as zero-vehicle households. Yet we know very little about these households and their travel behavior. In this study, therefore, we examine whether car deficits, like carlessness, are largely a result of financial constraint or of other factors such as built environment characteristics, household structure, or household resources. We then analyze the mobility outcomes of car-deficit households compared to the severely restricted mobility of carless households and the largely uninhibited movement of fully-equipped households, households with at least one car per driver. Data from the California Household Travel Survey show that car-deficit households are different than fully-equipped households. They have different household characteristics, travel less, and are more likely to use public transit. While many auto-deficit households have incomes that presumably enable them to successfully manage with fewer cars than adults, low-income auto-deficit households are—by definition—income constrained. Our analysis suggests that low-income car-deficit households manage their travel needs by carefully negotiating the use of household vehicles. In so doing, they travel far more than carless households and use their household vehicles almost as much as low-income households with at least one car per driver. These results suggest that the mobility benefits of having at least one car per driver are more limited than we had anticipated. Results also indicate the importance of transportation and employment programs to ease the potential difficulties associated with sharing cars among household drivers.

\"In 1938, Mercedes-Benz began production of the largest, most luxurious limousine in the world. A machine of frightening power and sinister beauty, the Grosser 770K Model 150 Offener Tourenwagen was 20 feet long, seven feet wide, and tipped the scales at 5 tons. Its supercharged, 230-horsepower engine propelled the beast to speeds over 100 m.p.h. while its occupants reclined on glove-leather seats ... Armor plated and equipped with hidden compartments for Luger pistols, the 770K was a sumptuous monster with a monstrous patron: Adolph Hitler and the Nazi Party ... Most of the 770Ks didn't make it out of the rubble of World War II. But several of them did. And two of them found their way ... to the United States\"--Provided by publisher.

A major barrier to the adoption of electric vehicle (EV) usage in the US is the current lack of charging infrastructure. This leads to concerns over running out of charge before a charging station can be reached, a phenomenon termed range anxiety. The purpose of the current study was to determine factors associated with range anxiety in a sample (N = 184) of EV owners. Participants completed an online survey which asked questions about their demographics and driving characteristics. Mental health and personality measures were also included. The outcome variable was self-reported range anxiety. At the univariate level length of daily commute, years of education, age, conscientiousness, neuroticism, and general level of psychological distress were all associated with range anxiety. When these variables were entered into a logistic regression only the general level of psychological distress and younger age were significant. These findings have several implications. The efforts of governments and industry towards reducing range anxiety have focused on infrastructure. This may not be sufficient to reduce range anxiety, as our findings indicate that range anxiety was not related to driving characteristics, but was instead best predicted by an individual's overall psychological distress. Therefore, techniques to address characterological anxiety and general psychological distress should also be utilized.

As one of the important sources of carbon emissions, the transportation industry should be given attention. This study investigates the relationship between vehicle ownership, economic growth, and environmental pressure on the Chongqing transportation industry (CQTI) based on CQTI data, then constructs a comprehensive regression model and couples the EKC curve and Tapio model for integrated analysis, and finally constructs a LEAP-Chongqing model to forecast CQTI from multiple perspectives. The innovations are that the multi-model examines the effects of different variables and has a better classification of transportation modes in scenario simulation. The results show that: (1) there is an inverse N-shaped relationship between car ownership, economic growth, and environmental pressure of CQTI; (2) every 1% of transportation output, urbanization rate, or car ownership will cause 0.769%, 0.111%, and 0.096% of carbon emission change, respectively; (3) gasoline, diesel and aviation kerosene consumption account for 80–90%, private cars cause 41–52% of carbon emissions, and the energy structure and transportation structure of CQTI are unreasonable; (4) the results of a multi-scenario simulation show that the energy saving and emission reduction effect of a single policy is not satisfactory, and the integration of energy saving and emission reduction measures should be strengthened.

Aggregate signatures are excellent in simultaneously verifying the validity of multiple signatures, which renders them highly suitable for bandwidth-constrained environments. The certificateless public key system is among the most advanced public key cryptosystems at present. Scholars have combined their advantages to develop certificateless aggregate signature schemes, which are applicable to the secure communication of Vehicular Ad-hoc Networks (VANETs). Recently, Cahyadi E F et al. put forward a certificateless aggregate signature scheme specifically designed for use in VANETs. Regrettably, through our strict security analysis, we discovered at least two major vulnerabilities in the signature scheme: a public key replacement attack and a malicious KGC (Key Generation Center) attack. To tackle these vulnerabilities, our article not only presents the methods of these attacks but also explores the fundamental reasons for their feasibility. Additionally, we propose specific improvement measures and show that the enhanced scheme retains its security under the random oracle model. The stability of the improved scheme depends on the computational complexity of the Diffie-Hellman problem. Finally, a comprehensive assessment involving security, computational cost, communicational cost, and calculational efficiency overhead highlights the excellent performance of our proposed solution.

Aiming to address the problems of traditional BP neural networks, which include their slow convergence speed and low accuracy, a vehicle ownership prediction model based on a BP neural network with particle swarm optimization is proposed. The weights and thresholds of the BP neural network are optimized by PSO to make the prediction results more accurate. Based on the current literature regarding BP neural networks’ ability to predict car ownership, a 9-10-1 BP neural network structure model is established. A traditional BP neural network and a PSO-optimized BP neural network are used to predict car ownership at the same time. In order to compare their prediction accuracy, a genetic algorithm (GA) and whale optimization algorithm (WOA) are additionally selected to optimize the BP neural network as a control group to predict car ownership. The data on China’s car ownership from 2005 to 2021 were collected as experimental data. The data from 2005 to 2016 were used as training data, and the remaining data were used as validation data for model prediction. The results show that the PSO-optimized neural network only undergoes three iterations of training, and the convergence accuracy reaches 1.41 × 10−8. The relative error between the predicted value of car ownership and the corresponding real value is between 0.023 and 0.083, and the decisive coefficient R2 is 0.96002, indicating that the neural network has better prediction ability and higher prediction accuracy for car ownership. The particle swarm optimization algorithm is used to optimize the weights and thresholds of the BP neural network, which solves the problems of the traditional BP neural network, including the ease with which it falls into the local minimum value and its slow convergence speed, and improves its prediction accuracy of car ownership. Compared with the results optimized by the genetic algorithm and whale optimization algorithm, the error of the BP neural network optimized by PSO is the smallest, and the prediction accuracy is the highest. Through the comparative analysis of training results, it can be seen that the PSO-BP prediction model has the best stability and accuracy.

Vehicle ownership modeling and prediction is a crucial task in the transportation planning processes which, traditionally, uses statistical models in the modeling process. However, with the advancement in computing power of computers and Artificial Intelligence, Machine Learning (ML) algorithms are becoming an alternative or a complement to the statistical models in modeling the transportation planning processes. Although the application of ML algorithms to the transportation planning processes—like mode choice, and traffic forecasting and demand modeling—have received much attention in research and abound in literature, scanty attention is paid to its application to vehicle ownership modeling especially in the context of small to medium cities in developing countries. Therefore, this study attempts to fill this gap by modeling vehicle ownership in the Greater Tamale Area (GTA), a typically small to medium city in Ghana. Using a cross sectional survey of formal sectors workers, data was collected between June–August 2018. The study applied nine different ML classification algorithms to the dataset using 10-fold cross-validation technique/s and the Cohen-Kappa static/statistic to evaluate the predictive performance of each of the algorithms, and the Permutation Feature Importance to examine the features that contribute significantly to the prediction of vehicle ownership in GTA. The results showed that Linear Support Vector Classification (LinearSVC) classifier performed well in comparison with the other classifiers with regards to the overall predictive ability of the classifiers. In terms of class predictions, K- Nearest Neighbors (KNN) classifier performs well for no-vehicle class whiles Linear Support Vector Classification (LinearSVC) and GaussianNB classifiers performs well for motorcycle ownership. LinearSVC and Logistic Regression classifiers performed well on the car ownership class. Also, the results indicated that travel mode choice, average monthly income, average travel distance to workplace, average monthly expenditure on transport, duration of travel to workplace, occupational rank, age, household size and marital status were significant in predicting vehicle ownership for most of the classifiers. These findings could help policies makers carve out strategies that would reduce vehicle ownership but improve personal mobility.