Destination Location Research Articles

Jittering: A Computationally Efficient Method for Generating Realistic Route Networks from Origin-Destination Data

Origin-destination (OD) datasets are often represented as ‘desire lines’ between zone centroids. This paper presents a ‘jittering’ approach to pre-processing and conversion of OD data into geographic desire lines that (1) samples unique origin and destination locations for each OD pair, and (2) splits ‘large’ OD pairs into ‘sub-OD’ pairs. Reproducible findings, based on the open source odjitter Rust crate, show that route networks generated from jittered desire lines are more geographically diffuse than route networks generated by ‘unjittered’ data. We conclude that the approach is a computationally efficient and flexible way to simulate transport patterns, particularly relevant for modelling active modes. Further work is needed to validate the approach and to find optimal settings for sampling and disaggregation.

Firm-Induced Migration Paths and Strategic Human-Capital Outcomes

Firm-induced migration typically entails firms relocating workers to fill value-creating positions at destination locations. But such relocated workers are often exposed to external employment opportunities at their destinations, possibly triggering turnover. We conceptualize the firm-induced migration path, consisting of the relocated workers’ place of origin and destination, as relevant in determining worker performance and turnover postrelocation. Using a unique data set from a large Indian technology firm that hires talent from both large cities and smaller towns, we document robust econometric patterns by exploiting the firm’s randomized assignment of workers to production centers across the country. These production centers are located in the largest technology cluster in India (Bangalore), smaller technology clusters, and noncluster locations. We find that the firm-induced migration path shapes both worker performance and turnover. Compared with workers from large cities, workers from smaller towns achieve higher performance when relocated to Bangalore than to other production centers, but are also more likely to join competing firms. Fine-grained data on employment and human-capital-augmentation opportunities at workers’ destination locations, and on socioeconomic conditions in workers’ places of origin, help us rule in an abductive explanation: across firm-induced migration paths, differences in external labor-market opportunities between workers’ places of origin and their destinations, as well as intrafirm skill-development opportunities at the destination, are related to heterogeneous human-capital outcomes. This paper was accepted by Alfonso Gambardella, business strategy. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2022.4361 .

Development of Tourist Village Website to Increase Tourism in Lake Toba

Currently, the Lake Toba area is included in one of the 10 priority tourism destination locations, so there are challenges to make changes to world tourism trends that utilize digital technology. So that several strategies must be carried out to capture these opportunities, one of which is through digitalization. For this reason, the development of a tourist village website in the Lake Toba area needs to be carried out to be able to take advantage of digital technology as a promotional media. The method applied in developing a tourist village website is the waterfall. The reason for implementing the waterfall method in developing a tourist village website is because all the requirements obtained from the client are clear and there are no more changes in the middle of implementation. The website displays good photography works and is equipped with the concept of story-telling. In addition to obtaining information about tourism in the Lake Toba area, the community can be directly involved as actors in tourism activities. Thus, a website manager will be formed in each tourist village of the Lake Toba Region. From the results of the development, it was found that the community's introduction to tourist villages in Lake Toba is increasing

Evaluating the Impact of the Influx of Syrian Refugees on Land Use/Land Cover Change in Irbid District, Northwestern Jordan

The refugee movement creates urban and environmental pressures at their destination locations. This pressure often presents in the form of Land Use/Land Cover (LULC) change. This study seeks to understand the impact of the Syrian refugees’ influence on changing the urban and agricultural land dynamics in Irbid district in northwestern Jordan from 1985 to 2021, including the period of the civil war in Syria, using Landsat Thematic Mapper (TM) images for the years 1985 and 2004, and the Landsat-8 Operational Land Imager (OLI) for the years 2013 and 2021. The Google Earth Engine (GEE) platform was used to conduct all image processing and perform calculations and classification analysis using the Random Forest (RF) approach. The study of the classified images compared LULC before and during the Syrian crisis using images from 1985, 2004, 2013, and 2021. The results show that the urban area increased. In parallel, agricultural land increased. During the Syrian refugee crisis, agriculture became a significant livelihood activity for Syrian refugees. In summary, the movement of the refugees to Irbid district caused an increased demand for land and housing, which accelerated the building and construction process.

Cultural Tourism Weakens Seasonality: Empirical Analysis of Chinese Tourism Cities

Cultural tourism is less seasonal than nature tourism. However, previous studies have mainly compared the tourist flow of scenic spots, and it is still unclear how cultural tourism affects regional tourism seasonality. This study investigated the seasonal patterns by analyzing the monthly inbound tourist flow of the 28 typical Chinese tourist cities from 2001 to 2012, and examined the effect of cultural tourism on weakening seasonality by using the random-response panel Tobit model. It was found that the seasonal patterns of inbound tourists present non-peak, one-peak, two-double, and three-peak regular fluctuations, and also have irregular fluctuations affected by emergencies and festivals. Cultural tourism can weaken the seasonality of regional tourism, while nature tourism products enhance tourism seasonality. Suitable travel times throughout the year and destination reception industry levels, locations, and external connections have a significant impact on the regional tourism seasonality, but climate comfort, foreign direct investment, and simply the number of hotels and international travel agencies are not significant for tourism seasonality.

Island Stories: Mapping the (im)mobility trends of slow onset environmental processes in three island groups of the Philippines

There is an immediate lack of people-centred empirical evidence investigating how slow onset events influence human (im)mobility across the globe. This represents an important knowledge gap that makes it difficult for climate policy to safeguard vulnerable populations (whether on the move or left behind). In this study, 48 qualitative focus group discussions in the Philippines elaborated around people’s (im)mobility pathways in the context of slow onset events. The selected collective storytelling approach effectively mapped out the (im)mobility trends of 12 different origin- and destination locations involving the perceptions of 414 women and men across six provinces on Luzon, the Visayas and Mindanao islands. The research findings delicately outlined people’s translocality and its interlinkage with their personal (im)mobility experiences. People described how slow onset events such as longer-term soil and water degradation often contributed to reduced livelihood sustainability that influenced their decisions to move or stay. At the very core of people’s narratives were the ways that the environmental changes and (im)mobility experiences influenced people’s wellbeing. Some people described how temporary migration could increase their social status and boost wellbeing after returning home. Others described adverse impacts on their mental health during their migration experiences due to loss of place, identity, food, and social networks. The research findings show how policy can better support those moving, hosting, or identifying as immobile, as well as where (geographically and socially) more assistance is needed.

Criteria to prioritize opportunities to shift paratransit trips to regular transit network – Montreal case study

IntroductionMobility is an important tool for the social inclusion of PWDs (people with disabilities). Unfortunately, the number of paratransit users, in most cities, is rapidly growing while resources remain limited. Paratransit is one of the rare public transport services that are adapted to the needs of users with disabilities. Still, it may be possible to shift some paratransit trips to the RTN (regular transit network). The aim of this paper is to develop and test criteria to identify paratransit trips that could be shifted to the RTN with the best potential of success. MethodsFour criteria are proposed to identify these trips. 1) The autonomy level of paratransit users: they must be able to use the RTN by themselves. 2) The universal design of each metro station, bus stop and vehicle. 3) The spatial location of both origin and destination of the trips and their proximity to the RTN, to avoid long walking/rolling distances. 4) The frequency of the trips: how many times over a year does a user performs a specific trip. We propose a method to evaluate these criteria. ResultsThese four criteria allow prioritizing paratransit trips that could be shifted the RTN with the most potential of success. With these criteria, the safety and the satisfaction of users could be maintained while reducing operation costs of paratransit services. The method has been applied to the paratransit trips of the STM (Montreal Transit Authority) and 15% (400,000 trips) of them could be shifted to the RTN in short term. ConclusionThis research is a step forward to shift paratransit trips to RTN. Still, the proposed criteria need to be refined and more criteria could be added to improve the prioritization of most promising paratransit trips. Nevertheless, the application of this methodology can assist stakeholders in making choices about the mobility services for PWDs.

Interrelated trips in the rural dial-a-ride problem with autonomous vehicles

Nowadays, the rural population is still dependent on motorized individual transportation due to the little offers of public transport there. Autonomous vehicles have the potential to close this mobility gap as shifting the task of driving from humans to machines facilitates new possibilities for vehicle sharing and lifts economical and service time limitations (e.g., labor costs and working hour regulations). However, mobility service operators are then probably responsible for serving the holistic travel demands of users. This might involve interrelated trips, where users ask for simultaneous arrival at meeting locations, where round trips involve a stay time at some destination locations, or the like. Accordingly, in this work, we present a problem variant of the dial-a-ride problem that allows for interrelated trips. We formulate the problem as a mixed-integer program and propose an adaptive variable neighborhood search heuristic (AVNS) based on a sophisticated solution representation. Our computational results for instances considering characteristics of rural areas reveal that the heuristic performs very well and that it is clearly superior to standard MIP solvers. We further investigate the impact of different types of trip interrelations on the solvability of the problem, service quality, and total transportation costs of the solutions.

Destination-Aware Focused Beam Routing (D-FBR): A routing protocol for underwater wireless sensor networks

Delay Tolerant Networks enable data transmission in different applications, mainly in environments where communication infrastructure is missing. The existence of links is casual and even when links are created, they are short in time and unstable/unreliable in terms of connectivity. Thus, underwater network applications tend to be delay tolerant. In this paper, we propose a mobility-aware routing protocol for underwater wireless sensor networks. The protocol is based on Focused Beam Routing (FBR) protocol and considers nodes’ next destination location when making forwarding decisions. The routing protocol is called Destination-aware Focused Beam Routing (D-FBR) protocol and we compare its performance with FBR protocol for different FBR angles and different applications. We use Delivery Probability (DP), Average Number of Hops (ANH), Overhead Ratio (OR) and simulate our scenarios by The ONE simulator. Simulation results show that, for D-FBR protocol, when FBR angle is 30 ∘ , OR and ANH is improved by 34.5 % and 23.1 %, respectively, while DP is 6.5 % lower, compared to FBR protocol. Both D-FBR and FBR outperform Epidemic Routing Protocol.

Comparative countries’ tourism technical efficiency assessment: A stochastic output distance function approach

This study aims at analysing the technical efficiency of the tourism industry worldwide. Using a sample of 111 countries worldwide from 2008 to 2016, we estimated the tourism industry technical efficiency score to measure of the industry performance from a translog output distance function modelling. Our results showed that high-income countries are more efficient because of higher qualified labour, and higher productivity of natural and cultural resources. Besides, our results support that African and Asian countries are less efficient than those from Europe and America. For international comparison purposes, our findings suggested that the level of income and the location of destinations should be incorporated as determinants and inputs of the tourism production function to the technical efficiency. In the political view, policymakers are encouraged to be aware of the income level of their citizens to improve the performance of their tourism sector.

Socio-cultural similarity with host population rather than ecological similarity predicts success and failure of human migrations.

Demographers argue that human migration patterns are shaped by people moving to better environments. More recently, however, evolutionary theorists have argued that people move to similar environments to which they are culturally adapted. While previous studies analysing which factors affect migration patterns have focused almost exclusively on successful migrations, here we take advantage of a natural experiment during World War II in which an entire population was forcibly displaced but were then allowed to return home to compare successful with unsuccessful migrations. We test two competing hypotheses: (1) individuals who relocate to environments that are superior to their place of origin will be more likely to remain—The Better Environment Hypothesis or (2) individuals who relocate to environments that are similar to their place of origin will be more likely to remain—The Similar Environment Hypothesis. Using detailed records recording the social, cultural, linguistic and ecological conditions of the origin and destination locations, we find that cultural similarity (e.g. linguistic similarity and marrying within one’s own minority ethnic group)—rather than ecological differences—are the best predictors of successful migrations. These results suggest that social relationships, empowered by cultural similarity with the host population, play a critical role in successful migrations and provide limited support for the similar environment hypothesis. Overall, these results demonstrate the importance of comparing unsuccessful with successful migrations in efforts understand the engines of human dispersal and suggest that the primary obstacles to human migrations and successful range expansion are sociocultural rather than ecological.

A Physical Internet (PI) based inland container transportation problem with selective non-containerized shipping requests

The imbalance of inbound and outbound containers generates large numbers of idle runs of container trucks in inland container transportation. In tradition, the trucking company acquires shipping requests from the spot market to increase profitability, with the precondition that both origin and destination locations of the acquired request are the same as the idle travel. Such precondition undoubtably leads to limited benefits and poor stability, given that shipping requests from the spot market are of high randomness. This paper proposes to adopt the idea of Physical Internet (PI) and assign an acquired request to several trucks, allowing transfers from one truck to another at logistics hubs. In this way, temporally and spatially dispersed idle resources could be consolidated to achieve the shipments of selective shipping requests, in a relay manner. To our knowledge, this paper is among the first to study the new paradigm of inland container transportation and formally model it as a PI-based selective vehicle routing problem (PI-SVRP). A mixed integer program (MIP) is presented and validated with CPLEX on small instances. In order to tackle real world instances, several novel heuristics have been designed particularly according to the special problem structure and embedded into an adaptive large neighborhood search (ALNS) framework. Finally, substantial instances are generated based on real data and a series of numerical studies are performed to derive managerial implications for practitioners.

Link Travel Time Estimation for Arterial Networks Based on Sparse GPS Data and Considering Progressive Correlations

Understanding complicated city traffic patterns has been recognized as a critical goal by twenty-first-century urban planners and traffic management systems, resulting in a significant rise in the quantity and variety of traffic data gathered. For example, in a growing number of large cities, taxi firms have begun collecting metadata for each vehicle trip, such as origin, destination, and travel duration. Taxi data offer information on traffic patterns, allowing the study of urban flow – what will traffic look like between two sites on a particular day and time in the future? This paper proposes a method based on sparse GPS probe data, that focuses on allocating travel time data to the different links traveled between GPS observations. This model incorporates the progressive spatial correlations between the links in a network. The main goal of this work is to show how we can consider progressive spatial correlations and improve our results more realistically with a simple adjustment in the previously known parametric methods. For estimating arterial travel time, the methodology is applied to a case study for the partial network of New York City-based on the data collected from the taxicabs in New York City, providing the locations of origins, destinations, and travel times. The model estimates quarter-hourly averages of urban link travel times using OD trip data. This study proposes a more accurate approach for estimating link travel times, that fully utilizes the partial information received from taxi data in cities.

Refined choice set generation and the investigation of multi-criteria transit route choice behavior

To best make informed public transportation decisions, agencies must be aware of how individuals interact with the transit system. The key question to consider is how do passengers get from Point A to Point B and which attributes of the transit trip have the largest influence on that choice? To more accurately understand passengers’ interaction with transit systems, a new method for transit route choice estimation is proposed. This method modifies and builds upon a schedule-based forward label setting multi-criteria shortest path algorithm by introducing an iterative trip elimination methodology. This new methodology yields high quality transit path choice sets with exact regeneration of 70.5% of the observed paths. Moreover, detailed temporal information on all types of network links (in-vehicle, walking, and waiting) are produced with reasonable computational time of 0.5 s per path calculation and 6.9 s per individual’s choice set generation. This increased specificity, in turn, heightens the validity and accuracy of the route choice model. Passenger information is sampled from a transit on-board survey containing origin–destination locations, demographic details, and trip-specific attributes. A multinomial logit model with stop-level path size correction term is estimated. This estimation yields a 67% recovery rate between the path with the highest estimated likelihood and the surveyed (taken) transit path. Furthermore, a transfer penalty of 28.8 min was estimated while the marginal rates of substitution for the waiting and walking time coefficients are in close alignment with similar values in the literature. Express bus routes were found to have a statistically significant negative impact on path utility for the lowest income thresholds while transitways (light rail, bus rapid transit, or commuter rail) had a positive associated perception for the highest household income class. Thus, support is found for the claim that transitways can potentially attract higher-income “choice” riders to the transit network. The results of this research can be used to improve ridership projections and highlight areas for policy improvements that could have the largest impact on retaining and attracting new passengers to the transit system.

Where Am I Parking: Incentive Online Parking-Space Sharing Mechanism With Privacy Protection

Sharing private parking spaces during their idle time periods has shown great potential for addressing urban traffic congestion and illegitimate parking problems in smart cities. In this article, aiming to address the online parking-space sharing issue while ensuring the privacy of customer parking destination locations, we propose a novel destination privacy-preserving online parking sharing (DPOPS) incentive scheme. In particular, the online parking-space sharing problem is formalized as a social welfare maximization problem in a two-sided market, where parking-space providers (PSPs) and customers are regarded as sellers and buyers. Then, novel threshold value-based rules are designed to determine winners, payments, and reimbursement. Finally, winners are matched by solving a mixed-integer nonlinear programming problem, aiming to minimize the distance between customer’s destination and allocated parking space. In addition, the location privacy of the customers’ destinations is protected by the Laplace mechanism. We prove that DPOPS achieves several economically effective properties and approximate differential privacy. We analyze the upper bound of the efficiency loss of our scheme. Extensive evaluation results demonstrate that our scheme can not only achieve good performance regarding social welfare, PSP satisfaction ratio, privacy preservation, and computation overhead but also leads to shorter travel distances for customers comparing to the baseline scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this article, we address the online parking-space sharing issue with considering the parking-space providers (PSPs) and customers’ individual utility while preserving the location privacy of customers’ destinations. Most of the previous works focused on designing a centralized mechanism for allocating parking spaces without considering the protection of the customers’ location privacy. In particular, we propose an online parking-space sharing scheme called DPOPS, including a novel threshold value-based winner determination rule and a parking-space allocation rule. The proposed scheme DPOPS allows the PSPs and customers submit their bids and asks according to their own willingness and is able to improve the utilization of private parking spaces during their idle time periods. Moreover, the location privacy of customers’ destinations is protected by the Laplace mechanism. The experiments demonstrate that the proposed approach outperforms the exponential-based scheme in terms of PSP satisfaction ratio and the travel distance for parking-space customer. The proposed scheme is helpful in managing the vacant parking space in a competitive market and can be readily implemented in the real-world online parking-space sharing systems.

Design o f Wireless Ad Hoc System u sing Adaptive Multi Hop Link State Optimal Routing Protocol

Now a days for the radio network communication multi-hop routing is used. This multi-hop routing technique covers larger coverage area. Therefore to reach at specific location data is transferred in form of packets from one node to other node. But for the transmission of radio signals over the large distance, large number of transreceivers are required and these transreceivers requires large power to operate. As a result, multi-hop routing can saves energy over separate routing. Therefore it is necessity to design a cost effective multi-hop routing technique for successful transmission of ratio packet data. In this paper a hop by hop adaptive link state optional routing (HALO) is explained. It is the first packet transmitting solution with hop by hop and link state routing, which reduces the cost of transporting data across a packet switch network[3]. The triple model is designed for multi hop packet routing. In this work each node of network iteratively and separately improves the small part of traffic bound. This algorithm finds the shortest path of specific location for every iteration and it is calculated by the marginal cost of the various links of network. The marginal link cost is used to calculate the shortest path between the node and the destination location. This marginal link cost is gathered from link state updates. The various networks changes are automatically identified by the adaptive method which is used in this paper. Due to this the exchange between the packets on wrong node is reduced over the overhead traffic. To validate these theoretical results the experimental evaluations and mathematical calculations are also reported in this work. Net beans java is the programmed use in this proposed project.

Disaster displacement and zoonotic disease dynamics: The impact of structural and chronic drivers in Sindh, Pakistan.

Projected increases in human and animal displacement driven by climate change, disasters and related environmental degradation will have significant implications to global health. Pathways for infectious disease transmission including zoonoses, diseases transmitted between animals and humans, are complex and non-linear. While forced migration is considered an important driver for the spread of zoonoses, actual disease dynamics remain under researched. This paper presents the findings of a case study investigating how disaster displacement affected zoonotic disease transmission risk following the 2010 'superfloods' in Sindh province, Pakistan. We interviewed 30 key informants and 17 household members across 6 rural communities between March and November 2019, supported by observational studies and a review of secondary data. Results were analysed using the ecosocial theoretical framework. Buffalo, cattle and goats were often the only moveable asset, therefore livestock was an important consideration in determining displacement modality and destination location, and crowded locations were avoided to protect human and animal health. Meanwhile however, livestock was rarely included in the humanitarian response, resulting in communities and households fragmenting according to the availability of livestock provisions. We found that rather than a driver for disease, displacement acted as a process affecting community, household and individual zoonotic disease risk dynamics, based on available resources and social networks before, during and after displacement, rooted in the historical, political and socio-economic context. We conclude that in rural Sindh, disaster displaced populations' risk of zoonoses is the result of changes in dynamics rooted in pre-existing structural and chronic inequalities, making people more or less vulnerable to disease through multiple interlinked pathways. Our findings have implications for policy makers and humanitarian responders assisting displaced populations dependent on livestock, with a call to integrate livestock support in humanitarian policies and responses for health, survival and recovery.

A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching Using Deep Reinforcement Learning

Significant development of ride-sharing services presents a plethora of opportunities to transform urban mobility by providing personalized and convenient transportation while ensuring the efficiency of large-scale ride pooling. However, a core problem for such services is route planning for each driver to fulfill the dynamically arriving requests while satisfying given constraints. Current models are mostly limited to static routes with only two rides per vehicle (optimally) or three (with heuristics) (Alonso-Mora <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2017), at least in the initial allocation while not ascertaining that opposite-direction rides are not grouped together. In this paper, we present a dynamic, demand aware, and pricing-based vehicle-passenger matching and route planning framework that (1) dynamically generates optimal routes for each vehicle based on online demand, pricing associated with each ride, vehicle capacities and locations. This matching algorithm starts greedily and optimizes over time using an insertion operation, (2) involves drivers in the decision-making process by allowing them to propose a different price based on the expected reward for a particular ride as well as the destination locations for future rides, which is influenced by supply-and-demand computed by the Deep Q-network. (3) allows customers to accept or reject rides based on their set of preferences with respect to pricing and delay windows, vehicle type and carpooling preferences. These (1-3) in tandem with each other enforce grouping rides with the most route-intersections together. (4) Based on demand prediction, our approach re-balances idle vehicles by dispatching them to the areas of anticipated high demand using deep Reinforcement Learning (RL). Our framework is validated using millions of trips extracted from the New York City Taxi public dataset; however, we consider different vehicle types and designed customer utility functions to validate the setup and study different settings. Experimental results show the effectiveness of our approach in real-time and large scale settings.

Are people who use active modes of transportation more physically active? An overview of reviews across the life course

ABSTRACT Regular physical activity prevents several non-communicable chronic conditions and premature mortality. The benefits of physical activity can be achieved through active transport, which refers to non-motorised/active means (e.g. walking, cycling, rollerblading) to move from one place to another. Active transport can be integrated into daily routines such as commuting to and from school and work. We undertook an overview of reviews to examine the association between active transport and physical activity across age groups. We aimed to provide a critical appraisal of research to date, and to identify research gaps that need to be addressed to advance the field. Eleven systematic reviews were included. Across children, youth and adults, active transport (mainly to school and work) was positively associated with physical activity and contributed approximately 5–45 additional minutes per day. The certainty of the evidence ranged from very low to moderate and was highest among studies that included both children and youth. There does not appear to be any clear differences by sex, measurement method for active transport or physical activity or review quality; however, some reviews noted that age and sex might moderate the association. Future research is needed to better understand the association among preschool-aged children and older adults, by sex and gender, in different socioeconomic and ethnic groups, and across the urban-rural spectrum. The field would benefit from more longitudinal and experimental research using device- and location-based measures to establish causality and separating location of destinations and mode of active transport (e.g. walking separate from cycling). Overall, evidence suggests that active transport is an important means to achieve daily physical activity recommendations.

Modular multi-dimensional tool for emergency evacuation including location-based social network data

ABSTRACT This paper presents the concept of a modular multi-dimensional tool (MMDT) for evacuation planning models. The goal of MMDT is to propose alternative route and destination locations that can be evaluated and compared to one another. The proposed tool can represent a very large number of scenarios and its strength is in its modularity and efficiency. The MMDT can be applied using both conventional evacuation models and decentralised personalised evacuation models based on Location-Based Social Networks (LBSN) to reduce overall evacuation times. Large-scale test cases using anonymous LBSN data illustrate the MMDT on several scenarios. Results indicate a significant reduction in evacuation times when using decentralised personal evacuation.