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Ubiquitous taxi trajectory data has made it possible to apply it to different types of travel analysis. Of interest is the need to allow someone to monitor travel momentum and associated congestion in any location in space in real time. However, despite an abundant literature in taxi data visualization and its applicability to travel analysis, no e...
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... though one-hour time interval is used to demonstrate the method in this work because one hour is easy to be recognized based on daily life experience, any other time interval can be defined and applied to this method. Hourly snapshots of the vector field, as estimated using the vector KDE, is shown in Fig. 5 for 24 hours on November 2 nd (Friday). For example, the snapshot at 00:00 represents vector field captured between 00:00 and 01:00. The height measures the magnitude of taxi density while the color refers to different directions of travel demand momentum. Based on this series of 3D visualizations, one can visually evaluate how overall ...
Citations
... For example, [24] proposes a queueing-based formulation to depict this problem in an on-demand mobility service, and the proposed algorithm can reduce the relocation cost largely. Specially, the projection from a continuously updated vector field of taxi travel momentum to the points of interest can be generated by [25]. For the vehicle-sharing operations, a rebalancing policy using cost function approximation is presented in [26], and the cost function is modeled as a p-median relocation problem with the minimum cost flow conservation. ...
At present, mobile charging stations (MCSs) are taken as an important complement of fixed charging stations. Currently, the strategy of MCSs is to move towards the electric vehicles to be charged (EVCs) only after being requested. To shorten the charging delay of EVCs and enhance the proportion of charged EVCs, idle MCSs should actively move to the areas with large potential charging demand rather than remaining stationary. The distribution of idle MCSs in different areas should be taken into account to prevent excessive idle MCSs from moving into the same areas simultaneously. To this end, we introduce the concept of charging demand force to depict the potential charging demand of EVCs, and then propose the Placement Strategy for Idle Mobile Charging Stations (PS-IMCS). In PS-IMCS, each idle MCS can measure the potential charging demand in neighboring areas through obtaining the resultant force composed of attraction force and repulsion force, and an MDP model is specially designed to make placement decisions for idle MCSs. Extensive simulations and comparisons demonstrate the performance superiority of PS-IMCS, i.e., the charging delay of EVCs can be significantly shortened, and the proportion of charged EVCs can be effectively enhanced.
... Vector field visualization [29,30] is a technique that portrays and presents vector data intuitively and understandably. Mathematically, a vector field is a mathematical concept that assigns a vector to each point in space, representing the direction and magnitude of a physical quantity at that point. ...
... Building upon endpoint clustering, they further conducted refined streamline clustering, effectively addressing the issue of inaccurate endpoint clustering results and enhancing the accuracy of streamline clustering. In the domain of extensive trajectory data, Liu et al. [29,30] proposed a population-based vector field approach for visualizing and representing spatiotemporal travel vectors, which are derived from the product of scalar travel density and vector travel velocity. This method utilizes vector kernel density estimated from observed trajectory samples to construct the field. ...
With the rapid growth of trajectory big data, there is a need for more efficient methods to extract, analyze, and visualize these data. However, existing research on trajectory big data visualization mainly focuses on displaying trajectories for a specific period or showing spatial distribution characteristics of trajectory points in a single time slice using clustering, filtering, and other techniques. Therefore, this paper proposes a vector field visualization model for trajectory big data, aiming to effectively represent the inherent movement trends in the data and provide a more intuitive visualization of urban traffic congestion trends. The model utilizes the motion information of vehicles to create a travel vector grid and employs WebGL technology for vector field visualization rendering. The vector field effects are effectively displayed by generating many particles and simulating their movements. Furthermore, this research also designs and implements congestion trend point identification and hotspot congestion analysis, thus validating the practicality and effectiveness of trajectory big data vector field visualization. The results indicate that compared to traditional visualization methods, the vector field visualization method can demonstrate the direction and density changes in traffic flow and predict future traffic congestion. This work provides valuable data references and decision support for urban traffic management and planning.
... However, their study lacks an aggregated level perspective, and it is hard to evaluate different social groups' disparity. The vector field method has the advantage of fully considering mobility from a population perspective [15][16][17]. However, it still lacks the third element, i.e., people's decisions, and furthermore, this method is rarely applied in environmental exposure studies. ...
... Liu et al. [16] extended the field theory to a population perspective, in which the vector field represents the momentum of travel demand, and therefore is also called a momentum vector field. The method is further applied by Liu et al. [17] in transportation research combined with the massive taxi global positioning system (GPS) location data from Beijing, China. A vector field fully covers space, each location has its intensities and directions, and it is also easy to calculate using vector algorithms. ...
... Here, we briefly describe the construction of the vector field of Liu et al. [16,17]. In the process, they split trajectory-based travel vectors by small-time slot and project them on 2D space as "travel momentum", of which the modulus and direction of the vectors are determined by the method of kernel density and vector addition, and then for studying taxi congestion, the researchers project the vectors on POIs in real-time. ...
Environmental exposure of people plays an important role in assessing the quality of human life. The most existing methods that estimate the environmental exposure either focus on the individual level or do not consider human mobility. This paper adopts a vector field generated from the observed locations of human activities to model the environmental exposure at the population level. An improved vector-field-generation method was developed by considering people’s decision-making factors, and we proposed two indicators, i.e., the total exposure indicator (TEI) and the average exposure indicator (AEI), to assess various social groups’ environmental exposure. A case study about the risky environmental exposure of coronavirus disease 2019 (COVID-19) was conducted in Guangzhou, China. Over 900 participants with various socioeconomic backgrounds were involved in the questionnaire, and the survey-based activity locations were extracted to generate the vector field using the improved method. COVID-19 pandemic exposure (or risk) was estimated for different social groups. The findings show that people in the low-income group have an 8% to 10% higher risk than those in the high-income group. This new method of vector field may benefit geographers and urban researchers, as it provides opportunities to integrate human activities into the metrics of pandemic risk, spatial justice, and other environmental exposures.
... With the advent of GPS-enabled floating cars such as taxis and buses, the road traffic data become more wide coverage, which provides opportunities to evaluate and analyse the traffic flow (Kerner et al. 2005;Tang et al. 2015;Liu et al. 2018). Regarding the relationship between traffic flow and traffic network (mainly road network), there are many studies as well. ...
Public transport system plays an important role in developing sustainable cities. The increasingly available transport big data such as the smart card data (SCD) provides new opportunities to shape deep light into this type of knowledge with unprecedented resolutions. However, most existing studies either only analyse the static structure of Public Transit Networks (PTNs) at small scale or utilize the SCD to model human mobility patterns. Little has been done to use SCD as a proxy of passenger flow to dynamically explore and evaluate the structure of large-scale PTNs. In this study, three types of large-scale PTNs are generated from two perspectives in Beijing, China: station-based network and line-based network of bus and subway, and the network merged from the above two, which represent bus, subway and the comprehensive PTN systems, correspondingly. In total, there are 35,674 bus stations, 1,574 directed bus lines, 278 subway stations and 36 directed subway lines in the study area. The overall network structure of the three types PTNs are examined and found to follow heavy tailed distributions (mostly power law), which is in good agreement with previous studies. Further, to explore the relationship between PTNs and passenger flow, the correlation analysis of three traditional centrality measures (i.e., degree, closeness & betweenness) of the PTNs are conducted on an hourly basis. The change patterns over 24 hours provides a new understanding of what we call “high frequency city”. Meanwhile, the modified three centrality measures are proposed to better evaluate the structural features of the large-scale PTNs and the relationship between passenger flow. The findings indicate that the modified technique for network centrality measures has a better performance than that of the traditional ones.
... 23 m: A total of 22 monitoring data from May 2019 to July 2019 are selected for analysis. When the serial number of the monitoring data is greater than 3, the monitoring data is taken as the center and three values are taken forward and backward as their neighborhoods to calculate the reliability of the monitoring data in their neighborhoods (Liu et al., 2018). ...
A remote monitoring system for geological exploration in a lava area based on GPS is designed. The system mainly collects geological exploration data in a lava area by means of multi-S integration through the data acquisition module of geological exploration in a lava area. Then the data collected are transmitted to the data analysis and management module of geological exploration data in a lava area by using a GPS network, and the data analysis and management module of geological exploration data in the lava area is exploited. The monitoring data processing method based on the unascertained filtering method is used to remove gross errors in geological exploration data of lava area, identify abnormal values in geological exploration data of lava area, and transmit the abnormal values to the GPS-based geological anomaly location module. The GPS-based geological anomaly location module locates the geological anomaly according to the abnormal values, thus completing the remote monitoring of the exploration of the lava area based on GPS. The test results show that when the system is applied to remote monitoring of geological exploration in a lava area, the positioning error of small soil caves is small, and the reliability of monitoring data is high, which meets the needs of remote monitoring of geological exploration in a lava area.
... Driver can identify the current person: The driver can use the QR code to identity whether the current person is his service user. [1][2][3][4][5][6][7][8][9][10][11]. No security channel required: The designed protocol uses a provably secure cryptographic protocol and does not require a security channel. ...
... the joint hash problem can be solved if9 ROexists.Definition 12 (2 nd Joint hash problem): then we say the 2 nd joint hash problem is solved. (The probability of solving this problem is Theorem 10 (Authentication from S to A in the callthe-selected-driver step): In our scheme, if an attacker can obtain valid ( then the 2 nd joint hash problem can be solved. ...
At present, the ride-hailing platform is numerous in the market. The platform scoring system also allows users to rate drivers and help users make decisions for drivers for reference. However, possible false scores may adversely affect user choice and trust. Previous studies on the ride-hailing system mostly focused on user waiting time improvement and scoring system construction, but lacked suggestions for improvement in scoring reliability. In order to improve the reliability, this paper proposes a new secure scoring mechanism, which is based on social network and can achieve the confidentiality and authentication of ratings. It provides the privacy of the scorer’s identity and allows users to identify and authenticate their friends’ ratings. The proposed method can be applied to a ride-hailing platform such as Uber and has proven to be secure and efficient. We also implemented this system on Android phones to prove the feasibility of the system. As far as we know, this paper is the first to propose a mobile ride-hailing evaluation system with privacy and authentication.
... M. Yao, D. Wang Transport Policy 69 (2018) 122-131 weekday activity patterns than their suburban counterparts, suggesting that individuals residing in suburban areas usually travel much farther to conduct various activities to meet personal and household needs, which induces greater need of private car ownership as well (Schönfelder and Axhausen, 2003;Páez et al., 2010;Tana et al., 2016). The higher car ownership level for residents living farther to CBD may also be attributed to the traffic congestion problem, since traffic congestion in areas close to CBD is severe and has become a big headache for Beijing residents (Liu et al., 2018). To some extent, this may discourage residents living close to CBD from buying cars and take the subway instead for daily travel. ...
Institutional factors, which are referred to social and economic norms and rules, define individuals' entitlement and access to opportunities and thus may play an important role in shaping individuals' mobility and travel behavior, especially in countries that are experiencing or have experienced the transition from a planned economy to market-oriented economy. Studies in Chinese cities show that danwei or type of work unit is an important institutional factor in explaining jobs-housing relationship and commuting behavior. We argue that, hukou or household registration (another institutional factor), may also explain the mobility and travel behavior of Chinese urbanites. Unlike population registration systems in many other countries, hukou, one of the most important institutional arrangements in contemporary China, determines an individual's entitlement to state-provided benefits and opportunities and plays a crucial role in defining access to housing, jobs, car ownership/usage, education, etc., which has far reaching implications for mobility and travel behavior. Using data collected from Beijing, we use structural equations modeling method to empirically test our hypothesis about the importance of hukou in explaining car ownership, travel time and transport mode choice for daily trips. Results show that hukou status has a significant impact on mobility and travel behavior of individuals. Specifically, local urban residents are found to have better home-work proximity and higher car ownership rate, travel more by non-motorized modes and spend less time on daily travel. This study provides insights into the complex relationships among hukou, built environment, mobility and travel behavior in urban China. The research findings can be used to assist planners and policy-makers in developing effective strategies to promote sustainable urban development.
This study aims to understand the dynamic change in individual taxi drivers’ performance in terms of income and passenger-search performance. We analyzed the global positioning system (GPS) data of 14,170 taxi drivers from a taxi company in Singapore, covering a period of 24 months. Our empirical analyses show that (1) accumulated driving experience increases income and that (2) as taxi drivers accumulate driving experience, they are likely to find new passengers more efficiently by spotting better search areas. We also conducted a field study to extend our understanding of and identify other factors that were not considered in our estimation but could play pivotal roles in performance enhancement, including improved passenger-search routine. The implications of our findings for both theory and practice are discussed.
In prior research, a statistically cheap method was developed to monitor transportation network performance by using only a few groups of agents without having to forecast the population flows. The current study validates this "multi-agent inverse optimization" method using taxi GPS trajectories data from the city of Wuhan, China. Using a controlled 2062-link network environment and different GPS data processing algorithms, an online monitoring environment is simulated using the real data over a 4-hour period. Results show that using only samples from one OD pair, the multi-agent inverse optimization method can learn network parameters such that forecasted travel times have a 0.23 correlation with the observed travel times. By increasing to monitoring from just two OD pairs, the correlation improves further to 0.56.
In prior research, a statistically cheap method was developed to monitor transportation network performance by using only a few groups of agents without having to forecast the population flows. The current study validates this "multi-agent inverse optimization" method using taxi GPS probe data from the city of Wuhan, China. Using a controlled 2062-link network environment and different GPS data processing algorithms, an online monitoring environment is simulated using the real data over a 4-hour period. Results show that using only samples from one OD pair, the multi-agent inverse optimization method can learn network parameters such that forecasted travel times have a 0.23 correlation with the observed travel times. By increasing to monitoring from just two OD pairs, the correlation improves further to 0.56.
