Destination Distribution Research Articles

Evaluation of heuristics for the p-median problem: Scale and spatial demand distribution

The objective of the p-median problem is to identify p source locations and map them to n destinations while minimizing the average distance between destinations and corresponding sources. Several heuristic algorithms have been developed to solve this general class of facility location problems. In this study, we add to the current literature in two ways: (1) we present a thorough evaluation of existing classic heuristics and (2) we investigate the effect of spatial distribution of destination locations, and the number of sources and destinations on the performance of these algorithms for varying problem sizes using synthetic and real datasets. The performance of these algorithms is evaluated using the objective function value, time taken to achieve the solution, and the stability of the solution. The sensitivity of existing algorithms to the spatial distribution of destinations and scale of the problem with respect to the three metrics is analyzed in the paper. The utility of the study is demonstrated by evaluating these algorithms to select the locations of ad-hoc clinics that need to be set up for resource distribution during a bio-emergency. We demonstrate that interchange algorithms achieve good quality solutions with respect to both the execution time and cost function values, and they are more stable for clustered distributions.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution.

The observable outputs of many complex dynamical systems consist of time series exhibiting autocorrelation functions of great diversity of behaviors, including long-range power-law autocorrelation functions, as a signature of interactions operating at many temporal or spatial scales. Often, numerical algorithms able to generate correlated noises reproducing the properties of real time series are used to study and characterize such systems. Typically, many of those algorithms produce a Gaussian time series. However, the real, experimentally observed time series are often non-Gaussian and may follow distributions with a diversity of behaviors concerning the support, the symmetry, or the tail properties. It is always possible to transform a correlated Gaussian time series into a time series with a different marginal distribution, but the question is how this transformation affects the behavior of the autocorrelation function. Here, we study analytically and numerically how the Pearson's correlation of two Gaussian variables changes when the variables are transformed to follow a different destination distribution. Specifically, we consider bounded and unbounded distributions, symmetric and non-symmetric distributions, and distributions with different tail properties from decays faster than exponential to heavy-tail cases including power laws, and we find how these properties affect the correlation of the final variables. We extend these results to a Gaussian time series, which are transformed to have a different marginal distribution, and show how the autocorrelation function of the final non-Gaussian time series depends on the Gaussian correlations and on the final marginal distribution. As an application of our results, we propose how to generalize standard algorithms producing a Gaussian power-law correlated time series in order to create a synthetic time series with an arbitrary distribution and controlled power-law correlations. Finally, we show a practical example of this algorithm by generating time series mimicking the marginal distribution and the power-law tail of the autocorrelation function of real time series: the absolute returns of stock prices.

Real-Time Route Recommendations for E-Taxies Leveraging GPS Trajectories

Electric vehicles (EVs) currently face formidable challenges in promotion, i.e., short driving ranges, long charging times, and few charging stations, thereby limiting their acceptability to taxi drivers. Leveraging massive-scale taxi GPS trajectory data, we present a novel real-time route recommendation system for electric taxi (ET) drivers. Taxi travel knowledge, including the probability of picking up passengers and the distribution of destinations, is learned from the raw GPS trajectories. Considering the cascading effect of route decision making, consecutive ET actions are modeled with an action tree. The corresponding expected net revenue is estimated based on the learned knowledge. A prototype online system is developed for providing route recommendations, e.g., when to go to a charging station or cruise on certain roads. An experiment in Shenzhen demonstrates that the average daily net revenue of ET drivers is better than those of 76.2% of gasoline taxi drivers. The presented approach not only increases the revenue of ET drivers in the short term but also improves the viability of EVs in the long run.

The influence of high-speed rail on ice–snow tourism in northeastern China

Abstract Ice-snow tourism is trendy in the global tourism industry. High-speed rail improves the accessibility of these destinations. However, the influences of accessibility on regional economies are understudied. This study investigated changes in accessibility between urban areas and ice-snow tourist destinations after implementation of high-speed rail in northeastern China. Data on transportation networks, destinations, and economic factors were used to compute weighted mean travel times and daily accessibility, and a tourism-economy linkage model was utilized. After implementation, (1) the shortest intercity travel times were significantly shorter, (2) numbers of one-day and weekend trips significantly increased, (3) distribution of accessible tourist destinations expanded northward and southward, (4) isochrone analysis revealed that central cities promoted tourism development in nearby cities, and (5) intercity tourism-economy linkages increased. Accessibility to Shenyang was the most powerful influence on economic development. The tourism-economy linkages between Liaoyang and other cities were greatly enhanced between 2008 and 2018.

Effects of neighborhood environment, safety, and urban amenities on origins and destinations of walking behavior

ABSTRACT Walking increases physical activity and reduces obesity. Walking behavior is influenced by many different factors, including urban safety. This article studies origin and destination choices of walking behavior in Salt Lake City using GIS and statistical analyzes.We find that the origins, and destinations are influenced by different neighborhood environmental factors. Street connectivity and public parks increase the number of destinations, but have little impact on the number of origins. The number of origins increases in commercial areas with compact design. In terms of safety, we find that walking behavior is influenced by robbery events rather than traffic accidents. Furthermore, a geographical weighted regression model suggests that bus stops and population density have strong positive effects in high-density areas such as the downtown areas. Our study suggests that the current analytical framework on walkability is more suitable to explain the spatial distribution of walking trip destinations than the origins.

Clustering Passenger Trip Data for the Potential Passenger Investigation and Line Design of Customized Commuter Bus

Customized commuter bus (CCB) is a kind of innovative public transit service launched starting in 2013 in many cities all over the world. It is designed to meet the direct travel demands of commuters who have similar origin and destination locations during their long-distance commuting trips at peak hours. To identify the origin and destination distribution of potential CCB passengers, this paper proposes a pair wise density-based spatial clustering algorithm. With the proposed algorithm, the method to extract the potential CCB passengers from regular bus passengers based on the bus smart card data is introduced. Meanwhile, the discovered hot locations of potential CCB passengers can be regarded as the candidate locations of CCB stops and can be used to set candidate CCB lines. Finally, the demand survey data collected from the actual registered CCB passengers are applied to verify the accuracy and feasibility of the clustering results obtained by the proposed algorithm. The related findings could guide bus operators to design CCB lines and allocate vehicle capacities on different lines.

Penentuan Jarak Terpendek Pada Jalur Pengiriman Musae Chips Dengan Menggunakan Algoritma Genetika

Combination of good path distribution by land can optimize travel time and costs. However, not all of these path distribution combinations will provide the best solution. The study was conducted to determine the distribution of goods so that the best solution is achieved. To simplify the process of determining the goods distribution channel, it is supported by software development. Genetic algorithms that have reliability in producing optimal solutions can be used to solve this problem. The application of the genetic algorithm method is applied in software. In the software that is built, there are several inputs needed, namely: cities destination distribution as the initial chromosome number, number of generations, crossover probability and probability of mutation. The result of processing is a combination of goods distribution lines to be taken which represent the solution to this problem. Only the best chromosomes will be given as a result. Through the software that was built, the determination of goods distribution lines is expected to be better and can optimize the time and cost of travel. Based on the research, 5 road combinations are used as chip distribution routes used for testing. This study results from the first fitness gene get the highest fitness 7.4, fitness lowest 5.6 and the second gene get the highest fitness 9.3, fitness lowest 5.6.

The perceived importance and objective measurement of walkability in the built environment rating

The built environment plays an important role in shaping physical activities and furthering a healthy lifestyle. An obesogenic environment, which promotes obesity through uninviting neighborhood design, can cause sedentary living and environmentally induced inactivity, particularly by reducing walking. Existing measures of walkability consider the distribution and features of potential destinations, but fail to account for key aspects of the built environment design, pedestrian preferences, or various reasons for walking. In this paper, we propose a new assessment method, the Perceived importance and Objective measurement of Walkability in the built Environment Rating (POWER), incorporating pedestrian preferences derived from a walking preference survey. By conducting a university campus-based case study, we developed a quantitative method, the customized analytic hierarchy process, to analyze data from the survey. The customized analytic hierarchy process was used to calculate the perceived importance of various factors. In addition, objective measurements were collected and processed from Geographic Information System (GIS) data and fieldwork. Using both perceived importance and objective measurements, we created a POWER map showing the most walkable and unwalkable places on campus. The outcome of this case study reveals the nuanced spatial variations with regard to walkability. The proposed integral measure creates an improved method for rating the walkability of the built environment. Future applications and limitations are also discussed.

Распределение лесов по целевому назначению в Ленском районе Республики Саха (Якутия)

Распределение лесов по целевому назначению в Ленском районе Республики Саха (Якутия)

Efficient multi-vehicle navigation based on trajectory vector features considering non-uniform destination distribution for emergency evacuation

In recent years, large-scale events have been held frequently, and more and more people are participating in these activities. When encountering an emergency, it is necessary to quickly evacuate the participating people. A reasonable traffic evacuation plan is an important part of the efficient evacuation. However, the uneven geographical distribution of a large number of vehicles and participants poses a challenge for efficient evacuation planning. An efficient emergency evacuation plan needs to minimise evacuation time and traffic congestion. Reasonable route selection and traffic flow assessment are key to the evacuation plan. In this paper, we propose an efficient multi-vehicle navigation method based on trajectory vector features considering non-uniform destination distribution for emergency evacuation. First, we employ the CrossRank algorithm to extract the real-time state vector of roads and then navigate the vehicle based on these state vectors. In addition, spatial diversity theory is introduced into our model for the non-uniform distribution of multiple vehicle evacuation. We conduct a series of multi-vehicle navigation simulation experiment on a real taxi trajectory data set. The experimental results demonstrate that our approach is effective and efficient.

Efficient multi-vehicle navigation based on trajectory vector features considering non-uniform destination distribution for emergency evacuation

In recent years, large-scale events have been held frequently, and more and more people are participating in these activities. When encountering an emergency, it is necessary to quickly evacuate the participating people. A reasonable traffic evacuation plan is an important part of the efficient evacuation. However, the uneven geographical distribution of a large number of vehicles and participants poses a challenge for efficient evacuation planning. An efficient emergency evacuation plan needs to minimise evacuation time and traffic congestion. Reasonable route selection and traffic flow assessment are key to the evacuation plan. In this paper, we propose an efficient multi-vehicle navigation method based on trajectory vector features considering non-uniform destination distribution for emergency evacuation. First, we employ the CrossRank algorithm to extract the real-time state vector of roads and then navigate the vehicle based on these state vectors. In addition, spatial diversity theory is introduced into our model for the non-uniform distribution of multiple vehicle evacuation. We conduct a series of multi-vehicle navigation simulation experiment on a real taxi trajectory data set. The experimental results demonstrate that our approach is effective and efficient.

Factors influencing Chinese tourist flow in Japan – a grounded theory approach

ABSTRACTThis study examined the factors influencing Chinese tourist flow in Japan. The study used a grounded theory to obtain insight into the tourist flow through text analysis of 96 online travel diaries and used these data to propose a model of factors. The model constitutes five categories: tourist condition (travel purpose, time budget, travel companion, prior visit experience, and opinions of others), destination characteristic (destination resource and distribution of destinations), transportation characteristic (transportation expense and transportation network), macro environment (visa policy and political relationship), and unforeseen circumstance (weather condition and fortuitous event). The study established a model for comprehensive understanding factors influencing tourist flow at inter-destination level.

Power-law distribution in an urban traffic flow simulation

We found power-law behavior in the distribution of traffic on road segments in urban traffic simulations using digitized map of Kobe city in Japan as an example of an actual road network. As a comparison, we performed simulations using an artificial random road network and Manhattan-type road network. Similar power-law behavior was confirmed in the former, but not the latter. The behavior appeared robustly with or without traffic congestion, which suggests that its origin is not the interaction between vehicles. The power-law exponent was fitted using least squares method and obtained as -1.1 for Kobe city and the random road network, with optimization to avoid traffic congestion. The result did not change with the use of a different origin and destination distribution. From these results, one of the reasons that caused the power-law behavior was considered to be the randomness of the road network connection and edge lengths, whose fluctuations are obvious both in Kobe city and the random road network, unlike the grid network.

Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach

Trip purpose inference is critical in transportation demand management (TDM) as well as traffic congestion alleviation. However, destination choice can be affected by a variety of factors, many of which are difficult to determine (e.g. socio-demographics). Besides, the spatio-temporal variation and correlation inherent in travel patterns further intensify the difficulty of understanding destination choice behavior. To this end, this research proposes a Bayesian hierarchical approach for modeling the destination choice behavior through time and space. The proposed method can take into account both the unavailable factors and spatio-temporal correlations by introducing random fields. Moreover, the implementation of the Integrated Nested Laplace Approximations (INLA) combined with the Stochastic Partial Differential Equation (SPDE) makes it computationally feasible to model large-scale spatio-temporal correlation structures. The model is further applied to two-week data from more than 8000 taxis in Harbin. The empirical results indicate that the proposed approach is capable to capture spatio-temporal variability in destination distribution, and the inclusion of spatial and temporal random effects is of great help to improve the model performance. The case study also examines how the land-use types influence the destination choice. It is believed that the modeling method and the exploratory spatial–temporal analysis of destination distribution in this study can enriches the methodologies for travel demand modeling as well as decision support for transport policy development.

K-means cluster analysis of tourist destination in special region of Yogyakarta using spatial approach and social network analysis (a case study: post of @explorejogja instagram account in 2016)

This research was conducted in Department of Statistics Islamic University of Indonesia. The data used are primary data obtained by post @explorejogja instagram account from January until December 2016. In the @explorejogja instagram account found many tourist destinations that can be visited by tourists both in the country and abroad, Therefore it is necessary to form a cluster of existing tourist destinations based on the number of likes from user instagram assumed as the most popular. The purpose of this research is to know the most popular distribution of tourist spot, the cluster formation of tourist destinations, and central popularity of tourist destinations based on @explorejogja instagram account in 2016. Statistical analysis used is descriptive statistics, k-means clustering, and social network analysis. The results of this research were obtained the top 10 most popular destinations in Yogyakarta, map of html-based tourist destination distribution consisting of 121 tourist destination points, formed 3 clusters each consisting of cluster 1 with 52 destinations, cluster 2 with 9 destinations and cluster 3 with 60 destinations, and Central popularity of tourist destinations in the special region of Yogyakarta by district.

Taxi trips distribution modeling based on Entropy-Maximizing theory: A case study in Harbin city—China

Understanding Origin–Destination distribution of taxi trips is very important for improving effects of transportation planning and enhancing quality of taxi services. This study proposes a new method based on Entropy-Maximizing theory to model OD distribution in Harbin city using large-scale taxi GPS trajectories. Firstly, a K-means clustering method is utilized to partition raw pick-up and drop-off location into different zones, and trips are assumed to start from and end at zone centers. A generalized cost function is further defined by considering travel distance, time and fee between each OD pair. GPS data collected from more than 1000 taxis at an interval of 30 s during one month are divided into two parts: data from first twenty days is treated as training dataset and last ten days is taken as testing dataset. The training dataset is used to calibrate model while testing dataset is used to validate model. Furthermore, three indicators, mean absolute error (MAE), root mean square error (RMSE) and mean percentage absolute error (MPAE), are applied to evaluate training and testing performance of Entropy-Maximizing model versus Gravity model. The results demonstrate Entropy-Maximizing model is superior to Gravity model. Findings of the study are used to validate the feasibility of OD distribution from taxi GPS data in urban system.

Making places: the role of Arctic cruise operators in ‘creating’ tourism destinations

ABSTRACTThis paper investigates how Arctic expedition cruise operators ‘create’ tourism destinations and diversify tourism space through itinerary making. The study proposes a typology of perceived values of a tourism destination and explores what value dimensions are critical in deciding on cruising locations. The typology helps to explain why certain locations became popular tourism destinations and why tourism space in the Arctic is so diversified. The analysis shows that ‘creation’ and distribution of Arctic expedition cruise destinations is dominated by functional costs and benefits, related mostly to sailing conditions and shipping logistics. Emotional costs (e.g. perceived risks) also play significant roles and prevail over emotional benefits (e.g. perceived achievement) and social factors, which were found least important.

An Improved Regional Traffic Volume Dynamic Feedback Control

The connection between traffic congestion and regional demand has become a consensus. A reasonable road network inflow control is essential to prevent the occurrence of congestion. With a consideration of deployment of detectors, an improved macroscopic fundamental diagram-based traffic volume dynamic control method via the feedback control is proposed in this study. Based on the origin–destination distribution of trips and connectivity of nodes, a novel method is adopted to identify the key nodes, on which the detectors are laid. An artificial neural network is adopted to predict traffic volume of those sections without detectors. As a case study, the proposed methodology is applied and estimated via simulation study to the regional road network which is located in the downtown area of Nanning, China. Adequate survey and analysis are carried out under the current road traffic conditions. It is proved that the regional traffic volume dynamic control can ensure steady and orderly regional traffic flow, and enhance the mobility during saturated traffic conditions.

The main paths of eTourism: trends of managing tourism through Internet

ABSTRACTDrawing from the tourism and hospitality literature, this paper highlights the main research trajectories and themes in the context of eTourism. We use main path analysis to investigate the development trend of the synergistic relationship between innovative tourism applications and the new technologies. Further, the literature is classified into six groups through discovering the coherence structure in a citation network. The analyses show that research on websites, blogs, and social media on tourism is experiencing accelerated growth, which hints that there is a pendulum shift whereby the information and negotiation power asymmetry between the supply and demand sides are moving from provider-centric to consumer-centric. This study also elucidates the associated market disintermediation phenomenon and long tail effects on the distribution of destinations.

A comparative study of automotive trip characteristics between older drivers and others among densely inhabited district and other areas

This paper examines older driver’s automotive trip (abbreviation: trip) characteristics which include trip frequency, trip length, destination distribution, and non-home-based (NHB) trips. A two-month experiment of 108 participants was carried out to collect GPS tracking data in Aichi Prefecture, Japan. To identify the effect of living area, a comparative analysis between older drivers and others is conducted in densely inhabited district (DID, i.e., urban) and other areas (non-DID, i.e., suburban, rural, etc), separately. The present study found that there was no significant difference between the trip characteristics of older drivers and others who were living in DID. Thus, we suggest that the education of safety driving and the recommendation of public transportation should be given to DID-living older drivers. However, the results of non-DID reflected that older drivers’ trip frequency, trip length, destination, and NHB trips rate were shorter and lower than others’. This implies that electric vehicles may be suitable for promotion among older drivers in suburban and rural area. Furthermore, the regression analysis confirmed that “older driver” was a significant independent variable on trip frequency, trip length, and NHB trips, and there were interaction effects between “older driver” and “living areas” on all trip characteristics.