Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. ROUTING FOR SPATIAL/GEOGRAPHICAL MOVEMENTS OF OBJECTS <s> Due to increased public awareness on global climate change and other energy and environmental problems, a variety of strategies are being developed and used to reduce the energy consumption and environmental impact of roadway travel. In advanced traveler information systems, recent efforts have been made in developing a new navigation concept called “eco-routing,” which finds a route that requires the least amount of fuel and/or produces the least amount of emissions. This paper presents an eco-routing navigation system that determines the most eco-friendly route between a trip origin and a destination. It consists of the following four components: 1) a Dynamic Roadway Network database, which is a digital map of a roadway network that integrates historical and real-time traffic information from multiple data sources through an embedded data fusion algorithm; 2) an energy/emissions operational parameter set, which is a compilation of energy/emission factors for a variety of vehicle types under various roadway characteristics and traffic conditions; 3) a routing engine, which contains shortest path algorithms used for optimal route calculation; and 4) user interfaces that receive origin-destination inputs from users and display route maps to the users. Each of the system components and the system architecture are described. Example results are also presented to prove the validity of the eco-routing concept and to demonstrate the operability of the developed eco-routing navigation system. In addition, current limitations of the system and areas for future improvements are discussed. <s> BIB001 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. ROUTING FOR SPATIAL/GEOGRAPHICAL MOVEMENTS OF OBJECTS <s> The rise of GPS-equipped mobile devices has led to the emergence of big trajectory data. In this paper, we study a new path finding query which finds the most frequent path (MFP) during user-specified time periods in large-scale historical trajectory data. We refer to this query as time period-based MFP (TPMFP). Specifically, given a time period T, a source v_s and a destination v_d, TPMFP searches the MFP from v_s to v_d during T. Though there exist several proposals on defining MFP, they only consider a fixed time period. Most importantly, we find that none of them can well reflect people's common sense notion which can be described by three key properties, namely suffix-optimal (i.e., any suffix of an MFP is also an MFP), length-insensitive (i.e., MFP should not favor shorter or longer paths), and bottleneck-free (i.e., MFP should not contain infrequent edges). The TPMFP with the above properties will reveal not only common routing preferences of the past travelers, but also take the time effectiveness into consideration. Therefore, our first task is to give a TPMFP definition that satisfies the above three properties. Then, given the comprehensive TPMFP definition, our next task is to find TPMFP over huge amount of trajectory data efficiently. Particularly, we propose efficient search algorithms together with novel indexes to speed up the processing of TPMFP. To demonstrate both the effectiveness and the efficiency of our approach, we conduct extensive experiments using a real dataset containing over 11 million trajectories. <s> BIB002 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. ROUTING FOR SPATIAL/GEOGRAPHICAL MOVEMENTS OF OBJECTS <s> The rapidly growing number of crowdsourcing platforms generates huge volumes of volunteered geographic information (VGI), which requires analysis to reveal their potential. The huge volumes of data appear as an opportunity to improve various applications, including routing and navigation services. How existing techniques for dealing with Big Data could be useful for the analysis of VGI remains an open question, since VGI differs from traditional data. In this paper, we focus on examining the latest developments and issues associated with big data from the perspective of the analysis of VGI. This paper notably presents our new architecture for exploiting Big VGI in event service processing in support to optimization of routing service. In addition, our study highlights the opportunities that are created by the emergence of Big VGI and crowdsourced data on improving routing and navigation services, as well as the challenges that remain to be addressed to make this a reality. Finally, avenues for future research on the next generation of collaborative routing and navigation services are presented. <s> BIB003 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. ROUTING FOR SPATIAL/GEOGRAPHICAL MOVEMENTS OF OBJECTS <s> In mobile ad-hoc cognitive radio networks (CRNs), end-to-end paths with available spectrum bands for secondary users may exist temporarily, or may never exist, due to the dynamism of the primary user activities. Traditional CRN routing algorithms, which typically ignore the intermittent connectivity of network topology, and traditional mobility-assisted routing algorithms, which generally overlook the spectrum availability, are obviously unsuitable. To tackle this challenge, we propose a Mobility-Assisted Routing algorithm with Spectrum Awareness (MARSA) to select relays based on not only the probability that a node meets the destination but also the chance at which there exists at least one available channel when they meet. To the best of our knowledge, this paper is the first to bring the idea of mobility-assisted routing to deal with the intermittently connected attribute of mobile ad-hoc CRNs, and the first to enhance the mobility-assisted routing by considering the temporal , spatial, and spectrum domains at the same time. Our simulation results demonstrate the superiority of MARSA over traditional algorithms in intermittently connected mobile CRNs. <s> BIB004 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. ROUTING FOR SPATIAL/GEOGRAPHICAL MOVEMENTS OF OBJECTS <s> When planning routes, drivers usually consider a multitude of different travel costs, e.g., distances, travel times, and fuel consumption. Different drivers may choose different routes between the same source and destination because they may have different driving preferences (e.g., time-efficient driving v.s. fuel-efficient driving). However, existing routing services support little in modeling multiple travel costs and personalization—they usually deliver the same routes that minimize a single travel cost (e.g., the shortest routes or the fastest routes) to all drivers. We study the problem of how to recommend personalized routes to individual drivers using big trajectory data. First, we provide techniques capable of modeling and updating different drivers' driving preferences from the drivers' trajectories while considering multiple travel costs. To recommend personalized routes, we provide techniques that enable efficient selection of a subset of trajectories from all trajectories according to a driver's preference and the source, destination, and departure time specified by the driver. Next, we provide techniques that enable the construction of a small graph with appropriate edge weights reflecting how the driver would like to use the edges based on the selected trajectories. Finally, we recommend the shortest route in the small graph as the personalized route to the driver. Empirical studies with a large, real trajectory data set from 52,211 taxis in Beijing offer insight into the design properties of the proposed techniques and suggest that they are efficient and effective. <s> BIB005 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. ROUTING FOR SPATIAL/GEOGRAPHICAL MOVEMENTS OF OBJECTS <s> Rapidly increasing volumes of GPS data collected from vehicles provide new and increasingly comprehensive insight into the routes that drivers prefer. While routing services generally compute shortest or fastest routes, recent studies suggest that local drivers often prefer routes that are neither shortest nor fastest, indicating that drivers value route properties that are diverse and hard to quantify or even identify. We propose a routing service that uses an existing routing service while exploiting the availability of historical route usage data from local drivers. Given a source and destination, the service recommends a corresponding route that is most preferred by local drivers. It uses a route preference function that takes into account the number of distinct drivers and the number of trips associated with a route, as well as temporal aspects of the trips. The paper provides empirical studies with real route usage data and an existing online routing service. <s> BIB006 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. ROUTING FOR SPATIAL/GEOGRAPHICAL MOVEMENTS OF OBJECTS <s> Eco-routing is a vehicle navigation method that aims to minimize fuel or energy consumption for a given trip. It is based on a hypothesis that we can trade extra travel time for lower consumption. While the hypothesis was experimentally verified the design of a method that would fully exploit its potential proves challenging. Current solutions hinge on assumption that energy spent on any given road does not change in time. We challenge validity of this assumption by studying performance of such methods in detailed second-by-second simulation that pronounces the time-dependencies. This allows us to quantify the real savings attainable with current eco-routing. <s> BIB007 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. ROUTING FOR SPATIAL/GEOGRAPHICAL MOVEMENTS OF OBJECTS <s> Route choice in multimodal networks shows a considerable variation between different individuals and the current situational context. Personalization and situation awareness of recommendation algorithms are already common in many areas, e.g., online retail. However, most online routing applications still provide shortest distance or shortest travel-time routes only, neglecting individual preferences, as well as the current situation. Both aspects are of particular importance in a multimodal setting as attractivity of some transportation modes, such as biking, which crucially depend on personal characteristics and exogenous factors, such as the weather. As an alternative, this paper introduces the FAVourite rOUte Recommendation (FAVOUR) approach to provide personalized, situation-aware route proposals based on three steps: first, at the initialization stage, the user provides limited information (home location, work place, mobility options, sociodemographics) used to select one out of a small number of initial profiles. Second, based on this information, a stated preference survey is designed in order to sharpen the profile. In this step, a mass preference prior (MPP) is used to encode the prior knowledge on preferences from the class identified in step one. Third, subsequently, the profile is continuously updated during usage of the routing services. The last two steps use Bayesian learning techniques in order to incorporate information from all contributing individuals. The FAVOUR approach is presented in detail and tested on a small number of survey participants. The experimental results on this real-world dataset show that FAVOUR generates better quality recommendations w.r.t. alternative learning algorithms from the literature. In particular, the definition of the MPP for initialization of step two is shown to provide better predictions than a number of alternatives from the literature. <s> BIB008
BD can play an important role in developing efficient traffic routing schemes. The increase in number of vehicles using location-based services and uncertainty in people's movement from one location to another demand us to develop more accurate, cost-effective, and energy efficient traffic routing schemes  . This requires a thorough and deep analysis of the currently available big volumes of data and sharing the available information with users/devices/vehicles in real-time  . 1) IMPORTANT TYPES OF SPATIAL BIG DATA ROUTING  In relation to routing of SBD, a promising work is presented in  , where routing is divided in several interesting categories such as eco-routing BIB001 , BIB007 , personalized routing BIB005 , BIB008 , and vehicle routing with user-generated trajectory data BIB006 , BIB002 as shown in figure 3 . In ecorouting, the best route for vehicles is chosen based on fuel efficiency. Other factors such as road situations, traffic and VOLUME 6, 2018 weather conditions, and amount of luggage in a vehicle can also be considered. Further, two approaches based on histogram and graph theory to represent various case studies related to eco-routing are presented and several interesting results are shown. Different from eco-routing, personalized routing presented in   do not provide any further details. Third type of routing presented in  considers temporal conditions (such as familiar/unfamiliar surroundings) and popularity index (rated by various local travelers) in selecting the best route. A ''trajectory database'' is created which provides trip suggestions to drivers in real-time. This database has routes which are rated/scored by various drivers. The results of trajectory database are compared with the results provided by the routing service (such as driver's navigation/GPS) and the route with maximum match is selected. Similar kind of routing concepts are highlighted in BIB004 and BIB003 as well. Based on the above concepts, we categorize the existing literature on big data routing in numerous categories discussed in the next subsections. Our work mostly focuses on (but not limited to) the research work done in the past two to three years.