The ability to predict how changes in the walking environment will affect the pedestrian flow is important to the designers of buildings and other constructed facilities. These changes can act on an individual pedestrian directly by diverting him from his preferred route, and indirectly through their effect on the other pedestrians. This paper presents a model for the interactions between pedestrians which is intended for use in a graphical computer simulation. The problem is treated by developing a simulation model for the movements of individual pedestrians under the hypothesis that if the behaviour of individuals can be adequately modelled, and the appropriate distribution of pedestrian types is employed, their corporate behaviour will be realistic. Working at this level allows the user to collect data on individuals, and subsequently analyse the variability within and between different classes of pedestrian. The program runs on a microcomputer and uses interactive colour graphics to display the operation of the model and assist in the validation and verification of the model (a).

A micro simulation model for pedestrian flows

The interest in using drones in various applications has grown significantly in recent years. The reasons are related to the continuous advances in technology, especially the advent of fast microprocessors, which support intelligent autonomous control of several systems. Photography, construction, and monitoring and surveillance are only some of the areas in which the use of drones is becoming common. Among these, last-mile delivery is one of the most promising areas. In this work we focus on routing problems with drones, mostly in the context of parcel delivery. We survey and classify the existing works and we provide perspectives for future research.

Drone-aided routing: A literature review

In the wake of e-commerce and its successful diffusion in most commercial activities, last-mile distribution causes more and more trouble in urban areas all around the globe. Growing parcel volumes to be delivered toward customer homes increase the number of delivery vans entering the city centers and thus add to congestion, pollution, and negative health impact. Therefore, it is anything but surprising that in recent years many novel delivery concepts on the last mile have been innovated. Among the most prominent are unmanned aerial vehicles (drones) and autonomous delivery robots taking over parcel delivery. This paper surveys established and novel last-mile concepts and puts special emphasis on the decision problems to be solved when setting up and operating each concept. To do so, we systematically record the alternative delivery concepts in a compact notation scheme, discuss the most important decision problems, and survey existing research on operations research methods solving these problems. Furthermore, we elaborate promising future research avenues.

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Last-mile delivery concepts: a survey from an operational research perspective

Optimization for drone and drone-truck combined operations: A review of the state of the art and future directions

Two echelon vehicle routing problem with drones in last mile delivery

Recently, several prominent logistic companies in Europe and the USA are seriously considering the idea of using drones launched from trucks and working in parallel to deliver packages. In the relevant literature, a novel problem formulation called travelling salesman problem with drone has been introduced, and some modelling and solution approaches have been presented. Existing approaches are based on the main assumption that the truck can dispatch and pick up a drone only at a node, i.e. the depot or a customer location. Here, the authors present a novel approach aimed to maximise the drone usage in parcel delivering. The authors consider that a truck can deliver and pick a drone up not only at a node but also along a route arc (
 en route
). In this way, the operations of a drone are not strictly related to the customers' position, but it can serve a wider area along the route. The authors tested the proposed heuristic on benchmark instances and analysed the benefits introduced with the en route
 approach.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-its.2017.0227

En route truck–drone parcel delivery for optimal vehicle routing strategies

In this paper, a model based on Artificial Neural Network (ANN) has been applied to real estate appraisal. Moreover, an evaluation of ANN performances in estimating the sale price of residential properties has been carried out. Artificial Neural Networks (ANNs) are useful in modelling input-output relationships learning directly from observed data. This capability can be very useful in complex systems like the real estate ones where motivations, tastes and budget availability often do not follow rational behaviours. This study also analyses the impact of such key environmental conditions that represent a problem related to many industrial cities where pollution and landscaping consequences affect the real estate market and residential location choices. We have considered a set of asking price's houses collected in the urban area of Taranto (Italy) where the biggest European steel factory and the 2 nd industrial harbour are located.

A Neural Network based Model for Real Estate Price Estimation Considering Environmental Quality of Property Location

In this paper, a review of the main actions and policies that can be implemented to promote sustainable mobility is proposed. The work aims to provide a broad, albeit necessarily not exhaustive, analysis of the main studies and research that from different points of view have focused on sustainable mobility. The structure of the paper enables the reader to easily identify the topics covered and the studies related to them, so as to guide him/her to the related in-depth studies. In the first part of the paper, there is a preliminary analysis of the concept of sustainable mobility, the main transport policies implemented by the European Union and the USA, and the main statistical data useful to analyze the problem. Next, the main policies that can promote sustainable mobility are examined, classifying them into three topics: Environmental, socio-economic, and technological. Many of the policies and actions examined could be classified into more than one of the three categories used; for each of them, there is a description and the main literature work on which the topic can be analyzed in more detail. The paper concludes with a discussion on the results obtained and the prospects for research.

Sustainable Mobility: A Review of Possible Actions and Policies

User satisfaction based model for resource allocation in bike-sharing systems

In the field of Swarm Intelligence, the Bee Colony Optimization (BCO) has proven to be capable of solving high-level combinatorial problems, like the Flight-Gate Assignment Problem (FGAP), with fast convergence performances. However, given that the FGAP can be often affected by uncertainty or approximation in data, in this paper we develop a new metaheuristic algorithm, based on the Fuzzy Bee Colony Optimization (FBCO), which integrates the concepts of BCO with a Fuzzy Inference System. The proposed method assigns, through the multicriteria analysis, airport gates to scheduled flights based on both passengers’ total walking distance and use of remote gates, to find an optimal flight-to-gate assignment for a given schedule. Comparison of the results with the schedules of real airports has allowed us to show the characteristics of the proposed concepts and, at the same time, it stressed the effectiveness of the proposed method.

Mario Marinelli

Papers

En route truck–drone parcel delivery for optimal vehicle routing strategies

A Neural Network based Model for Real Estate Price Estimation Considering Environmental Quality of Property Location

Sustainable Mobility: A Review of Possible Actions and Policies

User satisfaction based model for resource allocation in bike-sharing systems

Solving the gate assignment problem through the Fuzzy Bee Colony Optimization