Intermediate Destinations Research Articles

A Set of Control Points Conditioned Pedestrian Trajectory Prediction

Predicting the trajectories of pedestrians in crowded conditions is an important task for applications like autonomous navigation systems. Previous studies have tackled this problem using two strategies. They (1) infer all future steps recursively, or (2) predict the potential destinations of pedestrians at once and interpolate the intermediate steps to arrive there. However, these strategies often suffer from the accumulated errors of the recursive inference, or restrictive assumptions about social relations in the intermediate path. In this paper, we present a graph convolutional network-based trajectory prediction. Firstly, we propose a control point prediction that divides the future path into three sections and infers the intermediate destinations of pedestrians to reduce the accumulated error. To do this, we construct multi-relational weighted graphs to account for their physical and complex social relations. We then introduce a trajectory refinement step based on a spatio-temporal and multi-relational graph. By considering the social interactions between neighbors, better prediction results are achievable. In experiments, the proposed network achieves state-of-the-art performance on various real-world trajectory prediction benchmarks.

The Vacation Planning Problem: A multi-level clustering-based metaheuristic approach

We study the Vacation Planning Problem (VPP), an extension of the Tourist Trip Design Problem which is suitable for modelling scenarios wherein a tourist wishes to explore a large geographical area (e.g. an administrative region), rather than a single urban destination. Further to deriving personalized daily tourist routes i.e., ordered visits to points of interest (POIs) that maximize tourist satisfaction (profit), VPP also recommends intermediate destinations along the trip (most typically, towns to stay overnight) and accommodation areas, as well as optimal distribution of trip days among the different destinations. Herein, we formally define the VPP and present an innovative multi-level clustering scheme to group POIs together and an efficient dynamic programming approach for solving the problem. The proposed method yields higher-profit results compared to an existing approach, as it adopts a sophisticated approach in rendering the geography of the region to be visited by the tourist.

Path Planning and Obstacle Avoidance for UAV in an Uncharted Environment

Abstract: The growing industry of unmanned aerial vehicles (UAV) requires an efficient and robust algorithm to decide the path of the UAV and avoid obstacles. The study of pathfinding algorithms is ongoing research not just useful in the domain of drones, but in other fields like video games (AI pathfinding), terrain traversal (mapped, unmapped, areal, underwater, land, etc.), and industries that require robots to deliver packages. This paper proposes a new pathfinding algorithm that aims to solve the problem of pathfinding in unknown 2-dimensional terrain. Based on a system of assumptions and using the help of a set of sensors aboard the UAV, the algorithm navigates the UAV from a start point to an endpoint while avoiding any shape or size of obstacles in between. To avoid multiple different types of “infinite loop” situations where the UAV gets stuck around an obstacle, a priority-based selector for intermediate destinations is created. The algorithm is found to work effectively when simulated in Gazebo on Robot Operating System (ROS). Keywords: Path Planning, UAV, Obstacle Avoidance, Drone Navigation, Obstacle Detection, Uncharted Environment.

A study of vehicle routing problem via trade-off ranking method

Vehicle routing defines selecting the minimum cost, distance, and/or time path from a depot to several alternatives for a goods or service to reach its destination. The objective of most routing problem is to minimize the total cost of providing the service. But other objectives also may come into play, particularly in the public sector. For emergency services, such as ambulance, police, and fire engine, minimizing the response time to an incident is of primary importance. A few routing algorithms do not use a deterministic algorithm to find the “best” route for a goods to get from its original source to its destination. Instead, to avoid congestion, a few algorithms use a randomized algorithm that routes a path to a randomly picked intermediate destination, and from there to its true destination. In this paper, the trade-off ranking method is used to solve for the vehicle routing treated as a conflicting multi-criteria problem. The integration of the trade-off ranking method into the vehicle routing problem gives another perspective on how to solve the problem, hence broadened the decision support system for the vehicle routing problem.

Improving Air Transportation by Using the Fuzzy Origin–Destination Matrix

The work is devoted to the development of new methods and algorithms to support decision making when planning air travel using uncertainties in the form of fuzzy numbers. The proposed approach makes it possible to define rational methods of choice: how to change the transport graph to better meet the needs of the population. This is particularly relevant in the context of the reduced demand for air travel caused by the pandemic and the need to switch from large to smaller aircraft types. The problem is solved by restoring the fuzzy origin–destination matrix of current statistics on air traffic between airports. The problem is that we do not know what proportion of passengers moving between the specified points are forced to use large transport hubs as intermediate destinations. To determine the validity of the origin–destination matrix, we build a number of optimization models to determine fuzzy intervals and search for correspondence with the maximum value of the membership function. Algorithmic and software search for the fuzzy origin–destination matrix and fuzzy ranking of potentially promising routes are developed. The perspective of the given approach is shown by an example of a task concerning a choice of new routes between regional airports.

Improvement of Multi-agent Routing Guidance with an Intelligent Traffic Light Scheduling and the Ability to Select Intermediate Destinations

Traffic congestion and route guidance are integral parts of urban development in large cities. How cars are routed and the traffic flowing have a direct impact on each other. Therefore, the first step is to determine a criterion for assessing the traffic situation. The type of vehicles should also be considered in routing. Emergency vehicles must arrive at their mission site as soon as possible. Public transportations must also travel according to their plans. Ordinary vehicle drivers can either choose a road as an intermediate destination (out of interest, pick up someone, etc.). In this paper, two new algorithms are proposed to 1) route with intermediate destination selection for ordinary vehicles, and 2) schedule traffic lights to decrease traffic density and routing delay. The first algorithm proposed an agent-based route guidance model that in addition to finding the least expected travel time (LET) routes, drivers could select a part of the route as intermediate destinations according to their interests, to raise their satisfaction level. The second algorithm considers the density of traffic flow and the presence of emergency vehicles. This algorithm evaluates the status of the traffic flow by fuzzy logic. The evaluation is conducted by considering traffic flow speed and density. The output of fuzzy logic is used by the Gradational Search Algorithm (GSA). GSA regards the status of the flow, the priority of the traffic flow, and the distance of the emergency vehicles to the traffic light. The simulation results prove that the proposed algorithms have better performances.

Modeling and Simulation of Departure Passenger’s Behavior Based on an Improved Social Force Approach: A Case Study on an Airport Terminal in China

The unprecedented growth of passenger throughput in large airport terminals highlights the importance of analyzing passengers’ movement to achieve airport terminal’s elaborate management. Based on the theory of original social force model, video data from a departure hall of a large airport terminal in China were analyzed to summarize passengers’ path planning characteristics. Then, a double‐level model was established to describe passengers’ path planning behaviors. At the decision level of the proposed model, the avoiding force model including common avoiding force and additional horizontal avoiding force was established on the basis of setting time and space limitations for taking avoiding action and was used to describe passengers’ path planning in close‐range space. At the tactical level of the proposed model, the route and node choice models were established to describe passengers’ path planning in long‐range space. In the route choice model, a distribution of intermediate destination areas was proposed, with detouring distance, pedestrian density, speed difference, and pedestrian distribution considered in choosing an intermediate destination area. In the node choice model, the walking distance, the quantity of people waiting, and luggage were considered in choosing a check‐in counter or security check channel. The main parameters of the proposed model were confirmed according to video data. Simulation results show that the proposed model can simulate departure passengers’ path planning behaviors at an acceptable accuracy level.

Privacy-Preserving Task Scheduling for Time-Sharing Services of Autonomous Vehicles

Sharing of autonomous vehicles between multiple users can potentially be the ultimate solution for increasing the efficiency of the transportation system. In a time-sharing scenario, autonomous vehicles (AV) owners share their vehicles to others at their unwanted times without incurring any human efforts. However, such sharing service requires the disclosure of users’ locations and route information, raising severe privacy concerns and issues. In this paper, we propose a privacy-preserving task scheduling scheme for time-sharing services of autonomous vehicles. First, we design a matching scheme that finds the feasible requesters for each available AV. Then, we propose a scheduling scheme using different approaches for assigning requesters to the AVs based on different system parameters. Both schemes work efficiently without requiring users to share their exact locations and route details while maximizing the AV owner profit and minimizing the requester cost. Specifically, our schemes enable an untrusted matching server to match owners and requesters using a set of intermediate destination (ID) locations in the travel paths. Only if the service can be given to the requester efficiently, the owner and the requester share the details of the routes. All the calculations for verification of the feasibility of the service are done on the untrusted server. We show the accuracy of our proposed scheme through extensive simulations on real data. The results confirm that our traffic-based ID selection scheme, with consideration of the traffic information and patterns in the area, outperforms the baseline scheme where the IDs are chosen randomly. Furthermore, the effectiveness of different scheduling schemes including greedy-based and first come first served are evaluated.

New model for addressing supply chain and transport safety for disaster relief operations

The supply chain and logistical operations uncertainty are the most significant part for both businesses and economic activities. This paper investigates multi-item multi-stage solid transportation problem with fuzzy-stochastic environment to maximize the total route safety under safety constraint, in which the unit transportation costs, availabilities, demands, conveyance capacities, safety factors and desire total safety measure are supposed to be fuzzy-stochastic in nature. The multi-stage solid transportation model is developed in such a way that the cargoes are transported from sources to destinations via intermediate destination centers (DCs), where the DCs for stage-1 is reduced to the supply points for stage-2 and DCs for stage-2 is reduced to the supply points for stage-3 and similarly the DCs for the stage-$$(\hbox {n}-1)$$ is converted to the supply points for stage-n. The smooth transportation in the developing countries or rural areas or disaster affected areas is difficult due to hilly region, bad road, insurgency activities, landslides, etc. and for this reason, the safety objective function is considered to maximize under additional safety constraints. Two reduction procedures are used to get the equivalent deterministic form of the fuzzy-stochastic model. In the first procedure, the mean expectation is calculated considering lower $$\alpha $$-cut, upper $$\alpha $$-cut and signed distance of fuzzy numbers. However, for the second procedure, credibility measure, mean chances and expectation are taken into account to find the deterministic equivalent of the fuzzy-stochastic events. The modified GRG technique (LINGO.13.0 optimization solver) is used to solve the reduced deterministic model. Finally, a numerical example is provided to illustrate the model and methodology.

Using path decomposition enumeration to enhance route choice models

Prediction of realistic routes is essential in travel behavior research that evaluates the effects of infrastructure design alternatives. Most proposed route choice models are based on additive link attributes. This paper investigates the decomposition of a given path in a graph into least cost components. This corresponds to finding the smallest number of intermediate destinations between which the traveler moved using the most efficient path. Minimum path decompositions are not unique and hence a single given path may result in multiple sets of intermediate destinations. This paper presents a technique to enumerate all possible decompositions of revealed paths and shows how to determine which road network nodes are preferentially used as intermediate destinations.This paper explains the decomposition enumeration process and focuses on a new algorithm to enumerate efficiently all path decompositions. We implement the algorithms on 500k predicted bikers routes in Amsterdam.

Destination and route choice models for bidirectional pedestrian flow based on the social force model

In this study, social force model (SFM) is extended by using a discretisation grid to permit pedestrians to change their desired speed directions dynamically. In reality, other pedestrians may obscure the visions of the behind pedestrians, so the behind pedestrians will be blocked if they insist to walk in the final desired directions calculated by the SFM. So, a dynamic destination choice model is established to provide pedestrians a series of available intermediate destinations. Based on the dynamic destination choice model, the authors use a discretisation grid to represent all the potential moving directions of pedestrians, and model the weight of every potential moving direction. The direction with the maximum weight is selected as the optimal route at that time step. Besides, pedestrians prefer to pass near to crowds with a low relative velocity and choose the low space occupancy route when several routes have the same pedestrian density. So, pedestrian speeds, space un-occupancy, route length, crowd density and object pedestrian ratio are used to develop the weight model. The modified SFM guarantees pedestrians to obtain an available and optimal route. Compared to other models, the proposed models can be used to reproduce the behavior of bidirectional pedestrians more really.

Uncovering Offshore Financial Centers: Conduits and Sinks in the Global Corporate Ownership Network

Multinational corporations use highly complex structures of parents and subsidiaries to organize their operations and ownership. Offshore Financial Centers (OFCs) facilitate these structures through low taxation and lenient regulation, but are increasingly under scrutiny, for instance for enabling tax avoidance. Therefore, the identification of OFC jurisdictions has become a politicized and contested issue. We introduce a novel data-driven approach for identifying OFCs based on the global corporate ownership network, in which over 98 million firms (nodes) are connected through 71 million ownership relations. This granular firm-level network data uniquely allows identifying both sink-OFCs and conduit-OFCs. Sink-OFCs attract and retain foreign capital while conduit-OFCs are attractive intermediate destinations in the routing of international investments and enable the transfer of capital without taxation. We identify 24 sink-OFCs. In addition, a small set of five countries – the Netherlands, the United Kingdom, Ireland, Singapore and Switzerland – canalize the majority of corporate offshore investment as conduit-OFCs. Each conduit jurisdiction is specialized in a geographical area and there is significant specialization based on industrial sectors. Against the idea of OFCs as exotic small islands that cannot be regulated, we show that many sink and conduit-OFCs are highly developed countries.

Distributed scheduling approach for dynamic evacuation networks

ABSTRACTThe aim of this paper is to propose a novel distributed scheduling model for evacuation route scheduling from buildings and out of an affected area. The model is based on a hybrid approach that is spatio-temporal algorithm with optimization models as subproblems. The proposed distributed scheduling approach is an iterative process optimizing the one-step arrival of objects to each intermediate destination nodes at a time. To illustrate such an approach, we consider the problem of finding and scheduling evacuation paths from an urban building and out of a predetermined neighbourhood of the building on foot; the evacuation route involves pathways such as corridors, and stairs in buildings and road networks and sidewalks outside the building, there is a predefined set of exit points out of the target building and out of the road network serving the building. A well-known efficient heuristic algorithm is selected as a reference for comparative analysis and to illustrate the outperformance of the proposed approach in large-scale scenarios. The key results are the step-based optimal route schedules and the competitive evacuation time provided by the proposed distributed scheduling approach.

Enumerating minimum path decompositions to support route choice set generation

Abstract: This paper concerns the structure of movements as were recorded by GPS traces and converted to routes by map matching. Each route in a transportation network corresponds to a collection of directed paths or cycles in a digraph. When considering only directed paths, corresponding to utilitarian trips, the path is not necessarily a shortest path between its origin and destination, and can be split up into a small number of segments, each of which is a shortest or least cost path. Two consecutive segments are separated by split vertices . Split vertices act as intermediate destinations in the mind of travellers who try to hop between them using minimum cost paths. Hence they provide useful information to build route choice models. In this paper we identify and enumerate all possible decompositions of a path into a minimum number of shortest segments. This gives us an indication of the importance of split vertices occurring in particular sets of revealed routes that belong either to a single traveller or to a specific group. The proposed technique allows for automatic extraction of frequently used intermediate destinations (way-points) from revealed preference data.

Dynamic network equilibrium for daily activity-trip chains of heterogeneous travelers: application to large-scale networks

Applications of dynamic network equilibrium models have, mostly, considered the unit of traffic demand either as one-way trip, or as multiple independent trips. However, individuals’ travel patterns typically follow a sequence of trips chained together. In this study we aim at developing a general simulation-based dynamic network equilibrium algorithm for assignment of activity-trip chain demand. The trip chain of each individual trip maker is defined by the departure time at origin, sequence of activity destination locations, including the location of their intermediate destinations and their final destination, and activity duration at each of the intermediate destinations. Spatial and temporal dependency of subsequent trips on each other necessitate time and memory consuming calculations and storage of node-to-node time-dependent least generalized cost path trees, which is not practical for very large metropolitan area networks. We first propose a reformulation of the trip-based demand gap function formulation for the variational inequality formulation of the Bi-criterion Dynamic User Equilibrium (BDUE) problem. Next, we propose a solution algorithm for solving the BDUE problem with daily chain of activity-trips. Implementation of the algorithm for very large networks circumvents the need to store memory-intensive node-to-node time-dependent shortest path trees by implementing a destination-based time-dependent least generalized cost path finding algorithm, while maintaining the spatial and temporal dependency of subsequent trips. Numerical results for a real-world large scale network suggest that recognizing the dependency of multiple trips of a chain, and maintaining the departure time consistency of subsequent trips provide sharper drops in gap values, hence, the convergence could be achieved faster (compared to when trips are considered independent of each other).

Effective methodology to derive strategic decisions from ESA exploration technology roadmaps

Top priorities in future international space exploration missions regard the achievement of the necessary maturation of enabling technologies, thereby allowing Europe to play a role commensurate with its industrial, operational and scientific capabilities. As part of the actions derived from this commitment, ESA Technology Roadmaps for Exploration represent a powerful tool to prioritise R&D activities in technologies for space exploration and support the preparation of a consistent procurement plan for space exploration technologies in Europe. The roadmaps illustrate not only the technology procurement (to TRL-8) paths for specific missions envisaged in the present timeframe, but also the achievement for Europe of technological milestones enabling operational capabilities and building blocks, essential for current and future Exploration missions. Coordination of requirements and funding sources among all European stakeholders (ESA, EU, National, and Industry) is one of the objectives of these roadmaps, that show also possible application of the technologies beyond space exploration, both at ESA and outside. The present paper describes the activity that supports the work on-going at ESA on the elaboration and update of these roadmaps and related tools, in order to criticise the followed approach and to suggest methodologies of assessment of the Roadmaps, and to derive strategic decision for the advancement of Space Exploration in Europe. After a review of Technology Areas, Missions/Programmes and related building blocks (architectures) and operational capabilities, technology applicability analyses are presented. The aim is to identify if a specific technology is required, applicable or potentially a demonstrator in the building blocks of the proposed mission concepts. In this way, for each technology it is possible to outline one or more specific plans to increase TRL up to the required level. In practice, this translates into two possible solutions: on the one hand, approved mission concepts will be complemented with the required technologies if the latter can be considered as applicable or demo; on the other, if they are neither applicable nor demo, new missions, i.e. technology demonstrators based on multidisciplinary grouping of key technologies, shall be evaluated, so as to proceed through incremental steps. Finally, techniques to determine priorities in technology procurement are identified, and methodologies to rank the required technologies are proposed. In addition, a tool that estimates the percentage of technologies required for the final destination that are implementable in each intermediate destination of the incremental approach is presented.

Architectural Movements: Journeys of an Inter-colonial Profession

Migrations and movements of architects are central to understanding the architectural development of the colonial world. These journeys are usually framed as a question of where the architect originated, to determine the knowledge they brought to a place, but without understanding that their professional journey might also encompass significant intermediate points and further destinations that, likewise, fuelled knowledge and experience. Through a lens of continental Australia as a point of connection, this paper examines the professional journeys of a number of individual architects between English-speaking settlements in the British Imperial century (c.1815–1915). Their careers are traced across various colonies of the British Empire and British concessions in Asia. This research demonstrates that numerous architects made these professional lateral movements. The paper establishes this as a phenomenon of significance in Antipodean architectural history, exploring factors that prompted such movement, and, to a limited extent, the relative impact of their presence in local architectural cultures. It shows, for the first time, a set of architectural connections that transcend colonial borders, where architects moved with relative professional ease across substantial distances. It suggests there is a significant, complex and interwoven collective architectural history – an “entwined history” – across British colonies and concessions, which deserves greater consideration and attention.

A Geography-Based Void-Bypassing Routing Protocol for Wireless Sensor Network

Geographic routing protocol (GRP) allows sensor node to select path solely depending on the position information of its neighbor nodes. However, the irregular distribution of sensor node in wireless sensor networks (WSN) gives rise to the problem that GRP using the greedy algorithm may suffer the problem of local extrema. Due to the routing voids, it tends to result in the failure of data transmission. To overcome this drawback and improve the control packet overhead and transmission delay of routing void, based on virtual coordinate mapping, this paper proposes an efficient void-bypassing routing protocol (EVRP). Through mapping the coordinates on a virtual circle for edge nodes, EVRP transforms the void edge's random structure into a regular one. After clustering and redundant scheduling, each node has an energy threshold. If a node's energy is lower than its threshold, it sends a failure message to its cluster head which then uses the intersection angle with the failure node and considers all its neighbors as preparatory boundary nodes. Each preparatory boundary node is also judged. Thus a virtual rectangle is constructed. Its ability of covering the void which engenders dynamic intermediate destination nodes can enhance the efficiency and success of bypassing routing void. Compared with RGP and GPSR, simulation and analysis results demonstrate that EVRP is superior in finding the shortest routing path with higher delivery ratio, less control packet overhead and energy consumption.

How to Apply Assignment Methods that were Developed for Vehicular Traffic to Pedestrian Microsimulations

Applying assignment methods to compute user-equilibrium route choice is very common in traffic planning. It is common sense that vehicular traffic arranges in a user-equilibrium based on generalized costs in which travel time is a major factor.Surprisingly travel time has not received much attention for the route choice of pedestrians. In microscopic simulations of pedestrians the vastly dominating paradigm for the computation of the preferred walking direction is set into the direction of the (spatially) shortest path.For situations where pedestrians have travel time as primary determinant for their walking behavior it would be desirable to also have an assignment method in pedestrian simulations.To apply existing (road traffic) assignment methods with simulations of pedestrians one has to reduce the non-denumerably many possible pedestrian trajectories to a small subset of routes which represent the main, relevant, and significantly distinguished routing alternatives.All except one of these routes will mark detours, i.e. not the shortest connection between origin and destination. The proposed assignment method is intended to work with common operational models of pedestrian dynamics. These – as mentioned before – usually send pedestrians into the direction of the spatially shortest path. Thus, all detouring routes have to be equipped with intermediate destinations, such that pedestrians can do a detour as a piecewise connection of segments on which they walk into the direction of the shortest path. One has then to take care that the transgression from one segment to the following one no artifacts are introduced into the pedestrian trajectory.

Are GIS-modelled routes a useful proxy for the actual routes followed by commuters?

Active commuting offers the potential to increase physical activity among adults by being built into daily routines. Characteristics of the route to work may influence propensity to walk or cycle. Geographic information system (GIS) software is often used to explore this by modelling routes between home and work. However, if the validity of modelled routes depends on the mode of travel used, studies of environmental determinants of travel may be biased.We aimed to understand how well modelled routes reflect those actually taken, and what characteristics explain these differences. We compared modelled GIS shortest path routes with actual routes measured using QStarz BT-Q1000X global positioning system (GPS) devices in a free-living sample of adults working in Cambridge and using varying travel modes. Predictors of differences, according to length and percentage overlap, between the two route sets were assessed using multilevel regression models and concordance coefficients.The 276 trips, made by 51 participants, were on average 27% further than modelled routes, with an average geographical overlap of 39%. However, predictability of the route depended on travel mode. For route length, there was moderate-to-substantial agreement for journeys made on foot and by bicycle. Route overlap was lowest for trips made by car plus walk (22%). The magnitude of difference depended on other journey characteristics, including travelling via intermediate destinations, distance, and use of busy roads.In conclusion, GIS routes may be acceptable for distance estimation and to explore potential routes, particularly active commuting. However, GPS should be used to obtain accurate estimates of environmental contexts in which commuting behaviour actually occurs. Public health researchers should bear these considerations in mind when studying the geographical determinants and health implications of commuting behaviour, and when recommending policy changes to encourage active travel.