Explore the vast range of titles available.

As technology continues to evolve, our society is becoming enriched with more intelligent devices that help us perform our daily activities more efficiently and effectively. One of the most significant technological advancements of our time is the Internet of Things (IoT), which interconnects various smart devices (such as smart mobiles, intelligent refrigerators, smartwatches, smart fire alarms, smart door locks, and many more) allowing them to communicate with each other and exchange data seamlessly. We now use IoT technology to carry out our daily activities, for example, transportation. In particular, the field of smart transportation has intrigued researchers due to its potential to revolutionize the way we move people and goods. IoT provides drivers in a smart city with many benefits, including traffic management, improved logistics, efficient parking systems, and enhanced safety measures. Smart transportation is the integration of all these benefits into applications for transportation systems. However, as a way of further improving the benefits provided by smart transportation, other technologies have been explored, such as machine learning, big data, and distributed ledgers. Some examples of their application are the optimization of routes, parking, street lighting, accident prevention, detection of abnormal traffic conditions, and maintenance of roads. In this paper, we aim to provide a detailed understanding of the developments in the applications mentioned earlier and examine current researches that base their applications on these sectors. We aim to conduct a self-contained review of the different technologies used in smart transportation today and their respective challenges. Our methodology encompassed identifying and screening articles on smart transportation technologies and its applications. To identify articles addressing our topic of review, we searched for articles in the four significant databases: IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Consequently, we examined the communication mechanisms, architectures, and frameworks that enable these smart transportation applications and systems. We also explored the communication protocols enabling smart transportation, including Wi-Fi, Bluetooth, and cellular networks, and how they contribute to seamless data exchange. We delved into the different architectures and frameworks used in smart transportation, including cloud computing, edge computing, and fog computing. Lastly, we outlined current challenges in the smart transportation field and suggested potential future research directions. We will examine data privacy and security issues, network scalability, and interoperability between different IoT devices.

The development of new routes and stations, as well as changes in land use, can have significant impacts on public transit ridership. Thus, transport departments and governments should seek to determine the level and spatio-temporal dependency of these impacts with the aim of adjusting services or improving planning. However, existing studies primarily focus on predicting ridership, and pay relatively little attention to analyzing the determinants of ridership from temporal and spatial perspectives. Consequently, no comprehensive cognition of the spatio-temporal relationship between station ridership and the built environment can be obtained from previous models, which makes them unable to facilitate the optimization of transportation demands and services. To rectify this problem, we have employed a Bayesian negative binomial regression model to identify the significant impact factors associated with entry/exit ridership at different periods of the day. Based on this model, we formulated geographically weighted models to analyze the spatial dependency of these impacts over different periods. The spatio-temporal relationship between station ridership and the built environment was analyzed using data from Beijing. The results reveal that the temporal impacts of most ridership determinants are related to the passenger trip patterns. Furthermore, the spatial impacts correspond with the determinants’ spatial distribution, and the results give some implications on urban and transportation planning. This analysis gives a common analytical framework analyzing impacts of urban characteristics on ridership, and extending researches on how we capture the impacts of urban and other factors on ridership from a comprehensive perspective.

Debates concerning residential population displacement in the context of gentrification remain vociferous, but are hampered by a lack of empirical evidence of the extent of the displacement occurring. The lack of quantitative evidence on gentrification-induced displacement and the difficulties in collecting it has long hampered the fight against it. Based on a systematic review of quantitative studies of the displacement associated with gentrification, this article considers how researchers have attempted to measure displacement using a range of statistical and mapping techniques reflecting the multi-dimensional character of gentrification. We note that these techniques often struggle to provide meaningful estimates of the number of individuals and households displaced by gentrification, something compounded by the lack of data available on a sufficiently granular temporal and spatial scale. Noting the limitations of extant methods, we conclude by considering the potential of more novel data sources and emergent methods involving the processing of larger amounts of (micro)data, as well as participatory GIS methods that involve affected communities themselves. This implies that whilst the quantitative study of displacement remains difficult, patterns and processes of displacement can be inferred through existing data sources, as well as data generated from those who themselves have experienced displacement. 关于绅士化背景下的居民人口被驱逐问题的辩论仍然激烈，但由于缺乏驱逐程度的经验证据而受到阻碍。缺乏关于绅士化导致的驱逐的定量证据以及收集这些证据的困难，长期以来一直阻碍着对抗这一现象的斗争。在对与绅士化有关的驱逐的定量研究进行系统回顾的基础上，本文考察了研究人员如何试图使用反映绅士化多维特征的一系列统计和制图技术来测量驱逐。我们注意到，这些技术往往难以对因绅士化而被驱逐的个人和家庭的数量提供有意义的估计，而缺乏足够精细的时间和空间尺度的数据又加剧了这种情况。注意到现有方法的局限性，我们最后考察了更新颖的数据源和能够处理更大量（微观）数据的应急方法的潜力，以及涉及受影响街区本身的参与式地理信息系统（GIS）方法。这意味着，虽然对驱逐问题的定量研究仍然很困难，但驱逐的模式和过程可以通过现有的数据源以及那些自己经历过驱逐的人所产生的数据来推断。

The influence of the built environment on travel behavior has been the subject of considerable research attention in recent decades. Scholars have debated the role of residential self-selection in explaining the associations between the built environment and travel behavior. The purpose of this study is to make a contribution to the literature by adopting the cross-lagged panel modeling approach to analyze a panel data, which scholars have recommended as the ideal design for studying the influence of the built environment on travel behavior accounting for the residential self-selection. To that objective, we collected activity-travel diary data from a sample of 229 households in Beijing before and after they moved from one residential location to another. We developed a two-wave structural equation model linking the residential built environment to travel behavior and taking into consideration travel-related attitudes before and after residential change. The modeling results show that individuals’ travel attitudes may change after a home relocation. We found no evidence of residential self-selection, but significant influence of the built environment on travel preference. Nevertheless, the direct influence of travel preference on travel behavior seems to be stronger than that of the built environment. As one of the very few studies to use panel data, this research presents new insights into the relationship between the built environment and travel behavior and the role of residential self-selection.

Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

To discover regularities in human mobility is of fundamental importance to our understanding of urban dynamics, and essential to city and transport planning, urban management and policymaking. Previous research has revealed universal regularities at mainly aggregated spatio-temporal scales but when we zoom into finer scales, considerable heterogeneity and diversity is observed instead. The fundamental question we address in this paper is at what scales are the regularities we detect stable, explicable, and sustainable. This paper thus proposes a basic measure of variability to assess the stability of such regularities focusing mainly on changes over a range of temporal scales. We demonstrate this by comparing regularities in the urban mobility patterns in three world cities, namely London, Singapore and Beijing using one-week of smart-card data. The results show that variations in regularity scale as non-linear functions of the temporal resolution, which we measure over a scale from 1 minute to 24 hours thus reflecting the diurnal cycle of human mobility. A particularly dramatic increase in variability occurs up to the temporal scale of about 15 minutes in all three cities and this implies that limits exist when we look forward or backward with respect to making short-term predictions. The degree of regularity varies in fact from city to city with Beijing and Singapore showing higher regularity in comparison to London across all temporal scales. A detailed discussion is provided, which relates the analysis to various characteristics of the three cities. In summary, this work contributes to a deeper understanding of regularities in patterns of transit use from variations in volumes of travellers entering subway stations, it establishes a generic analytical framework for comparative studies using urban mobility data, and it provides key points for the management of variability by policy-makers intent on for making the travel experience more amenable.

Landslides are a ubiquitous hazard in terrestrial environments with slopes, incurring human fatalities in urban settlements, along transport corridors and at sites of rural industry. Assessment of landslide risk requires high-quality landslide databases. Recently, global landslide databases have shown the extent to which landslides impact on society and identified areas most at risk. Previous global analysis has focused on rainfall-triggered landslides over short ∼ 5-year observation periods. This paper presents spatiotemporal analysis of a global dataset of fatal non-seismic landslides, covering the period from January 2004 to December 2016. The data show that in total 55 997 people were killed in 4862 distinct landslide events. The spatial distribution of landslides is heterogeneous, with Asia representing the dominant geographical area. There are high levels of interannual variation in the occurrence of landslides. Although more active years coincide with recognised patterns of regional rainfall driven by climate anomalies, climate modes (such as El Niño–Southern Oscillation) cannot yet be related to landsliding, requiring a landslide dataset of 30+ years. Our analysis demonstrates that landslide occurrence triggered by human activity is increasing, in particular in relation to construction, illegal mining and hill cutting. This supports notions that human disturbance may be more detrimental to future landslide incidence than climate.

Passenger satisfaction is critical to ridership growth of high speed rail (HSR). Each HSR trip includes at least four segments: access to HSR stations, waiting, line-haul, and egress from HSR stations. Satisfaction with any segment influences the HSR passenger experience. Previous studies often focus on passenger satisfaction with the line-haul segment, but overlook the effects of all four segments on overall HSR satisfaction, especially access and egress. Using a path analysis on the data collected from the Shanghai-Nanjing HSR corridor in 2016, this study explores the influence of access and egress segments on overall HSR satisfaction and the correlates of satisfaction with HSR access and egress segments. We find that HSR line-haul satisfaction dominates overall HSR satisfaction; HSR access and egress satisfaction together have an equivalent effect. Travel time and route familiarity are important to both access and egress satisfaction. Mode choice affects satisfaction with HSR egress, with egress by car carrying the largest utility of egress satisfaction, followed by rail transit, taxi, and then bus. Thus, to improve HSR experience, traveler information service and the integration of HSR with urban transportation system are critical.

Urban planning and management need accurate three-dimensional (3D) data such as light detection and ranging (LiDAR) point clouds. The mobile laser scanning (MLS) data, with up to millimeter-level accuracy and point density of a few thousand points/m2, have gained increasing attention in urban applications. Substantial research has been conducted in the past decade. This paper conducted a comprehensive survey of urban applications and key techniques based on MLS point clouds. We first introduce the key characteristics of MLS systems and the corresponding point clouds, and present the challenges and opportunities of using the data. Next, we summarize the current applications of using MLS over urban areas, including transportation infrastructure mapping, building information modeling, utility surveying and mapping, vegetation inventory, and autonomous vehicle driving. Then, we review common key issues for processing and analyzing MLS point clouds, including classification methods, object recognition, data registration, data fusion, and 3D city modeling. Finally, we discuss the future prospects for MLS technology and urban applications.