Stochastic Trip Planning in High Dimensional Public Transit Network

This paper proposes a generalised framework for density estimation in large networks with measurable spatiotemporal variance in edge weights. We solve the stochastic shortest path problem for a large network by estimating the density of the edge weights in the network and analytically finding the distribution of a path. In this study, we employ Gaussian Processes to model the edge weights. This approach not only reduces the analytical complexity associated with computing the stochastic shortest path but also yields satisfactory performance. We also provide an online version of the model that yields a 30 times speedup in the algorithm's runtime while retaining equivalent performance. As an application of the model, we design a real-time trip planning system to find the stochastic shortest path between locations in the public transit network of Delhi. Our observations show that different paths have different likelihoods of being the shortest path at any given time in a public transit network. We demonstrate that choosing the stochastic shortest path over a deterministic shortest path leads to savings in travel time of up to 40\%. Thus, our model takes a significant step towards creating a reliable trip planner and increase the confidence of the general public in developing countries to take up public transit as a primary mode of transportation

Emergency medical supplies scheduling during public health emergencies: algorithm design based on AI techniques

Based on AI technology, this study proposes a novel large-scale emergency medical supplies scheduling (EMSS) algorithm to address the issues of low turnover efficiency of medical supplies and unbalanced supply and demand point scheduling in public health emergencies. We construct a fairness index using an improved Gini coefficient by considering the demand for emergency medical supplies (EMS), actual distribution, and the degree of emergency at disaster sites. We developed a bi-objective optimisation model with a minimum Gini index and scheduling time. We employ a heterogeneous ant colony algorithm to solve the Pareto boundary based on reinforcement learning. A reinforcement learning mechanism is introduced to update and exchange pheromones among populations, with reward factors set to adjust pheromones and improve algorithm convergence speed. The effectiveness of the algorithm for a large EMSS problem is verified by comparing its comprehensive performance against a super-large capacity evaluation index. Results demonstrate the algorithm's effectiveness in reducing convergence time and facilitating escape from local optima in EMSS problems. The algorithm addresses the issue of demand differences at each disaster point affecting fair distribution. This study optimises early-stage EMSS schemes for public health events to minimise losses and casualties while mitigating emotional distress among disaster victims

The faster the better: On the shortest paths role for near real-time decision making of water utilities

Near real-time monitoring and control of critical infrastructure is essential for the operation and management of cities in a world that is, today, more complex and interconnected than ever. Such an infrastructure can be represented as complex networks an some of their related indices and statistics, many of them based on the shortest paths, play a pivotal role in the decision making for public services such as internet, energy or water. Particularly, the literature has shown that shortest paths are key for resilience and criticality assessment in a water distribution systems (WDS). This paper proposes a procedure to speed-up the computation of shortest paths in a WDS, as it can straightforwardly benefit any critical infrastructure. The proposal is based on a reduced dimension of a complex network representing any critical infrastructure. Despite the consequent decrease in the number of all possible paths in the network, the main advantage and novelty of this proposal is to continue finding the exact solution for the shortest paths. Experimental results show that the procedure brings a computational-time reduction consistently over 50% and up to 90% in some cases. In addition, the paper reveals how the use of shortest paths benefits WDS operation and management, as well as playing a key role in near real-time contamination detection and leakage control

How to Mitigate Traffic Congestion Based on Improved Ant Colony Algorithm: A Case Study of a Congested Old Area of a Metropolis

Old areas of metropolises play a crucial role in their development. The main factors restricting further progress are primitive road transportation planning, limited space, and dense population, among others. Mass transit systems and public transportation policies are thus being adopted to make an old area livable, achieve sustainable development, and solve transportation problems. Identifying old areas of metropolises as a research object, this paper puts forth an improved ant colony algorithm and combines it with virtual reality. This paper predicts traffic flow in Yangpu area on the basis of data obtained through Python, a programming language. On comparing the simulation outputs with reality, the results show that the improved model has a better simulation effect, and can take advantage of the allocation of traffic resources, enabling the transport system to achieve comprehensive optimization of time, cost, and accident rates. Subsequently, this paper conducted a robustness test, the results of which show that virtual traffic simulation based on the improved ant colony algorithm can effectively simulate real traffic flow, use vehicle road and signal resources, and alleviate overall traffic congestion. This paper offers suggestions to alleviate traffic congestion in old parts of metropolises. Document type: Articl

Tutorials at PPSN 2016

PPSN 2016 hosts a total number of 16 tutorials covering a broad range of current research in evolutionary computation. The tutorials range from introductory to advanced and specialized but can all be attended without prior requirements. All PPSN attendees are cordially invited to take this opportunity to learn about ongoing research activities in our field

Dynamic shortest path problem with travel-time-dependent stochastic disruptions : hybrid approximate dynamic programming algorithms with a clustering approach

We consider a dynamic shortest path problem with stochastic disruptions in the network. We use both historical information and real-time information of the network for the dynamic routing decisions. We model the problem as a discrete time nite horizon Markov Decision Process (MDP). For networks with many levels of disruptions, the MDP faces the curses of dimensionality. We rst apply Approximate Dynamic Programming (ADP) algorithm with a standard value function approximation. Then, we improve the ADP algorithm by exploiting the structure of the disruption transition functions. We develop a hybrid ADP with a clustering approach using both a deterministic lookahead policy and a value function approximation. We develop a test bed of networks to evaluate the quality of the solutions. The hybrid ADP algorithm with clustering approach signicantly reduces the computational time, while stil providing good quality solutions. Keywords: Dynamic shortest path problem, Approximate Dynamic Programming, Disruption handling, Clusterin

Dynaamisen kyydinjakosovelluksen reitinhakualgoritmin kehitt äminen

Vedia Taxi is a mobile application that makes it possible to share taxi rides with people going to the same direction. It uses an algorithm to combine the routes of different people to achieve this. However the algorithm can only handle cases where people leave from the same location and people cannot join from the route. The purpose of this work is to extend the algorithm to handle these cases. For the extended algorithm four routing services were compared. These routing services provide the raw route data and time estimates for the routes. Google Maps was chosen among these providers, because it had the most accurate time estimates for the routes. The raw routes from different providers had some differences, but all of them were adequate. While the Google Maps did not have the best route calculation time, it was crucial for the algorithm that the time estimates are as correct as they can be. The extended algorithm was tested using test searches that mimicked real life taxi rides, as there was not enough test data from real life rides made with the application. The performance of the algorithm was tested by using the same test searches. The test searches showcased that with the extended algorithm routes can be formed also when start locations are different. This also makes it possible to join along the route. The performance of the algorithm was highly dependant on the amount of time it took to calculate the raw routes. The calculation of routes took 92% and 98% of the running time of the algorithm. The average time to calculate raw route was 275ms and the evaluation of a single ride took 275-300ms.Vedia Taxi on mobiilisovellus, joka mahdollistaa taksikyytien jakamisen samaan suuntaan matkustavien ihmisten kesken. Jakamisen mahdollistaa algoritmi, joka yhdistelee eri ihmisten reittejä. Algoritmi pystyy kuitenkin yhdistämään vain reittejä, joissa kaikki ihmiset lähtevät samasta lähtöpisteestä. Ihmiset eivät myöskään voi liittyä kyytiin matkan varrelta. Työn tarkoituksena on jatkokehittää algoritmia niin, että kyytiin olisi mahdollista liittyä mistä tahansa. Uutta algoritmia varten vertailtiin neljää reitityspalvelua keskenään. Nämä reitityspalvelut tuottavat reittiohjeet ja aika-arviot reiteille. Google Maps valittiin parhaaksi reitityspalveluksi, koska sen aika-arviot reiteille olivat tarkimmat. Reittiohjeissa oli joitakin eroja eri reitityspalveluiden välillä, mutta kaikki niistä olivat tarpeeksi hyviä. Google Maps ei muodostanut reittejä kaikista nopeimmin, mutta algoritmin kannalta oli välttämätöntä, että aika-arviot olisivat mahdollisimman tarkkoja. Algoritmia testattiin käyttämällä kyytejä ja hakuja, jotka vastasivat todellisia taksikyytejä. Tämä lähestymistapa otettiin sen takia, että Vedia Taxi ei ollut tuottanut tarpeeksi oikeaa testimateriaalia. Algoritmin tehokkuutta mitattiin samoilla testikyydeillä ja -hauilla. Testihaut näyttivät, että uusi algoritmi pystyy yhdistämään reittejä niin, että ihmiset lähtevät eri aloituspisteistä. Uusi algoritmi mahdollisti myös sen, että ihmiset pystyvät liittymään kyytiin matkan varrelta. Algoritmin tehokkuus oli suuresti riippuvainen siitä, kuinka pitkä aika kului reittiohjeiden laskemiseen. Reittiohjeiden laskeminen kulutti 92%-98% algoritmin kokonaissuoritusajasta. Reittiohjeiden haku kesti keskimäärin 275ms ja yhden kyydin arviointi hakua vastaan kesti 275-300ms

Locating Compromised Data Sources in IoT-Enabled Smart Cities: A Great-Alternative-Region-Based Approach

Sensing devices acting as interconnected data sources are becoming increasingly ubiquitous in concepts of Internet of Things (IoT)-enabled smart cities, but they typically lack physical protection and are susceptible to being compromised. To address this issue, a great-alternative-region (GAR)-based approach for deploying network monitors to locate compromised data sources is proposed. The GAR concept is introduced according to the network topology and connectivity characteristics, and the GARs with the most complete connectivity are identified as the candidate monitor locations, thereby transforming the problem of monitor deployment into a traditional K-center problem. Based on the demonstrated relationship between the monitor locations and the locating accuracy, the optimization objective for reasonably deploying monitors is designed to minimize the maximum number of hops between the data sources and their nearest monitors, and the optimal deployment pattern is achieved using an improved genetic algorithm. Finally, simulation-based results are presented to illustrate the performance of this approach