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In the last two decades new emphasis has been given to the economic impact of geography, especially on the cost of being landlocked. From a development perspective, understanding the cost of being landlocked and its economic impact is critical, since one country of four in the world is landlocked (almost one out of three in Sub-Saharan Africa). Attempts to address the cost of being landlocked have mainly focused on regional and multilateral conventions aiming at ensuring freedom of transit, and on the development of regional transport infrastructure. The success of these measures has been limited, and many massive investments in infrastructure seem to have had a disappointing impact on landlocked economies. Although there may still be an infrastructure gap, this book, based on extensive data collection in several regions of the world, argues that logistics and trade services efficiency can be more important for landlocked countries than investing massively in infrastructure. Logistics have become increasingly complex and critical for firms' competitiveness, and a weakness in this field can badly hurt firms based in landlocked countries. This book proposes a revised approach to tackling the cost of being landlocked and a new analytical framework which uses a microeconomic approach to assess the trade and macroeconomic impacts of logistics. It takes into account recent findings on the importance of logistics chain uncertainty and inventory control in firms' performance. It argues that: (i) exporters and importers in landlocked developing countries face high logistics costs, which are highly detrimental to their competitiveness in world markets, (ii) high logistics costs depend on low logistics reliability and predictability, and (iii) low logistics reliability and predictability result mostly from rent-seeking and governance issues (prone to proliferate in low volume environments).

Considering that the charging behaviors of users of electric vehicles (EVs) (including charging time and charging location) are random and uncertain and that the disorderly charging of EVs brings new challenges to the power grid, this paper proposes an optimal electricity pricing strategy for EVs based on region division and time division. Firstly, by comparing the number of EVs and charging stations in different districts of a city, the demand ratio of charging stations per unit is calculated. Secondly, according to the demand price function and the principle of profit maximization, the charging price between different districts of a city is optimized to guide users to charge in districts with more abundant charging stations. Then, based on the results of the zonal pricing strategy, the time-of-use (TOU) pricing strategy in different districts is discussed. In the TOU pricing model, consumer satisfaction, the profit of power grid enterprises, and the load variance of the power grid are considered comprehensively. Taking the optimization of the comprehensive index as the objective function, the TOU pricing optimization model of EVs is constructed. Finally, the nondominated sorting genetic algorithm (NSGA-II) is introduced to solve the above optimization problems. The specific data of EVs in a municipality directly under the Central Government are taken as examples for this analysis. The empirical results demonstrate that the peak-to-valley ratio of a certain day in the city is reduced from 56.8% to 43% by using the optimal pricing strategy, which further smooth the load curve and alleviates the impact of load fluctuation. To a certain extent, the problem caused by the uneven distribution of electric vehicles and charging stations has been optimized. An orderly and reasonable electricity pricing strategy can guide users to adjust charging habits, to ensure grid security, and to ensure the economic benefits of all parties.

We examine the relationship between transportation access on the one hand and individuals’ employment and labor earnings on the other. We improve on existing studies by bringing a large national panel data set to bear on this question, attempting to disentangle the mechanisms by which individuals improve their economic standing and, finally, comparing the economic benefits to the direct costs of car ownership. To do this, we use nine waves from the Panel Study of Income Dynamics from 1999 to 2015. We find that access to a car is a strong predictor of future economic benefit for individuals, and that at very high levels of transit access, carless individuals can also fare equally well. Access to an automobile is strongly associated with employment, job retention, and earning more money over time. Though having a car is associated with economic benefits, owning and operating a car is expensive; yet, our findings suggest that the benefits may outweigh the costs for most people living outside neighborhoods with truly excellent transit service.

Car-sharing could have substantial benefits. However, there is not enough evidence about if more people choosing car-sharing would reduce private car usage or public transport demand. This work aims to bring forward some insights by studying short-term car-sharing choice behavior. A mode choice analysis is conducted first followed by a simulation analysis to evaluate modal substitution pattern. Policy implications are obtained in terms of the possible measures that could effectively bring down private car usage. The case study is Taiyuan-China; stated and revealed preference data are collected. Mixed nested logit models are developed to study the pooled SP/RP data. The analysis is conducted separately for a shorter trip case (2–5 km) and a longer trip case (more than 5 km) to examine if results would differ by distance. It is found that raising the cost of private car usage (travel cost, parking cost) should be prioritized for shorter trips since car is more difficult to be substituted when trip distance increases. Shorter trips also need such direct measures to help suppress the demand for private car when promoting a car-sharing service; otherwise car-sharing would attract more bus users instead. Longer trips need a more effective solution to bring down private car usage and that is discovered as making car-sharing service more appealing so that it can serve as a practical substitute to private car. A number of informative indicators (e.g. willingness to pay for travel time savings, direct and cross point elasticity) are also derived to enrich the findings.

Electric vehicles (EVs) are considered as a driving force behind the automotive industry’s transformation based on eco-friendliness and high energy efficiency. Unlike expectations, the diffusion of EVs is proceeding at a slow pace in Korea. This study therefore aims to identify the factors influencing the slow market diffusion of EVs from a socio-technical perspective, by comparing the perceptions of the experts and the individuals. We constructed 15 factors in the five dimensions including costs, automobile characteristics, charging conditions, policy instruments, and perceptions for the analytic hierarchy process analysis. Surveys were performed with 58 EV experts and 87 individuals with driver’s licenses in Korea. The results identified and prioritized charging concerns as the highest barrier in both groups, and burden of costs as another critical barrier in the individual group. All factors in charging concerns, burden of initial costs, insufficient performance, and insufficient financial incentives were identified as major influential factors in both groups. While, lack of non-financial supports (for experts), and burden of battery costs and lack of social empathy (for individuals) were ranked as other upper factors. Statistical analysis of the analysis of variance results revealed that the burden of costs was more of a hurdle to the individuals than to the experts. These results suggest implications for policy-making and practice in promoting a widespread EV market.

Carbon emissions from urban transportation significantly contribute to overall transportation emissions and are a major cause of the continuous rise in global temperatures. Understanding the spatial distribution and influencing factors of carbon emissions from operating vehicles can aid in formulating targeted policies and promoting emission reduction. To analyze the factors influencing urban traffic carbon emissions, we calculated emissions using trajectory data from operating vehicles in Shenzhen. We then used gradient boosting regression tree methods, specifically RF, XGBoost, and LightGBM models, to analyze the impact of the built environment on vehicle emissions. We used the XGBoost model for detailed factor analysis by comparing the models. The results indicate that bus stops, intersections, housing density, metro stops, and land use mix are the top five factors influencing emissions. When road density is 0–15 km/km 2 , the distance from the city center is 0–6 km, and the population exceeds 2000/km 2 , the built environment significantly reduces vehicle emissions.

Urban roads constitute most of the existing roads and they are directly managed by small administrations. Normally, these small administrations do not have sufficient funds or sufficient qualified personnel to carry out this task. This paper deals with an easy-implementation Pavement Management System (PMS) to develop strategies to maintain, preserve and rehabilitate urban roads. The proposed method includes the creation of the road network inventory, the visual surveys of the pavement and the evaluation of its condition by the Pavement Condition Index (PCI). The method intends to give a valid tool to road managers to compare alternative maintenance strategies and perform the priority analysis on the network. With this aim, the procedure assesses the Vehicle Operating Costs (VOC) by a written regression between PCI and International Roughness Index (IRI). The proposed method has several advantages because it can be easily adapted to various situations and it does not require a large amount of time and money for its implementation.

The importance of transport corridors for trade and development, including for some of the poorest countries in the world, is widely recognized in this book. A new consensus has also emerged that reducing trade costs and improving access to corridors is not just a matter of building infrastructure. The policies that regulate transport services providers and the movement of goods along corridors are important determinants of the social rate of return on such infrastructure investment. This book avoids optimistic assumptions regarding the prospects for new high-level agreements and decisions to facilitate transit or the possible benefits from increased use of technology. Instead, the authors argue that much can be done through the implementation of readily available existing tools. The use of these tools is often hampered by not only capacity constraints; but, equally if not more important, a lack of commitment. Political economic factors in both the landlocked countries and their transit neighbors must be recognized and addressed. This book offers examples of possible implementation strategies that, while challenging, should in principle help in overcoming these political economic constraints. The main message is that to bring about efficient trade corridors governments and stakeholders should focus on properly implementing the fiscal, regulatory, and procedural principles for international transit that encourage quality-driven logistics services. The various implementation challenges are the primary focus of this book.

Road traffic which results in significant time lags, increased fuel consumption, and financial losses, remains a noteworthy challenge in developed and developing countries. As a result, the Ethiopian Government and the City Administration of Addis Ababa have built extensive road networks and imposed restrictions on driving, vehicle acquisition and parking. However, despite all these efforts, drivers and passengers waste significant time on long traffic queues, resulting in unpredictable and delayed travel. The current study investigated the cost of travel time delay, vehicle operating costs, time reliability, and the factors influencing these variables. The study used questionnaires, measurements, and traffic counting techniques to collect data from nine road segments. The sample comprised 3240 participants. The cost functions of both drivers and passengers were examined using a multiple linear regression model, with estimation performed using ordinary least squares. According to the findings, the economic costs of congestion depend on the number of lanes, the length of the road segment, the volume of traffic, and the respondents’ income level. The study also revealed that travel, vehicle operation, and unreliability costs account for 74%, 6%, and 20%, respectively, of the total congestion costs.

Under extreme working conditions such as high-speed driving on roads with a large road surface unevenness coefficient, turning on a road with a low road surface adhesion coefficient, and emergency acceleration and braking, a vehicle’s stability deteriorates sharply and reduces ride comfort. There is extensive existing research on vehicle active suspension control, trajectory tracking, and control methods. However, most of these studies focus on conventional operating conditions, while vehicle stability analysis under extreme operating conditions is much less studied. In order to improve the stability of the whole vehicle under extreme operating conditions, this paper investigates the stability of a vehicle under extreme operating conditions based on linear quadratic regulator (LQR) control. First, a seven degrees of freedom (7-DOF) dynamics model of the whole vehicle is established based on the use of electromagnetic active suspension, and then an LQR controller of the electromagnetic active suspension is designed. A joint simulation platform incorporating MATLAB and CarSim was built, and the CarSim model is verified by real vehicle tests. Finally, the stability of the vehicle under four different ultimate operating conditions was analyzed. The simulation results show that the root mean square (RMS) values of body droop acceleration and pitch angle acceleration are improved by 57.48% and 28.81%, respectively, under high-speed driving conditions on Class C roads. Under the double-shift condition with a low adhesion coefficient, the RMS values of body droop acceleration, pitch acceleration, and roll angle acceleration are improved by 58.25%, 55.41%, and 31.39%, respectively. These results indicate that electromagnetic active suspension can significantly improve vehicle stability and reduce driving risk under extreme working conditions when combined with an LQR controller.