scholarly journals Computing Multi-Modal Journey Plans under Uncertainty

2019 ◽  
Vol 65 ◽  
pp. 633-674
Author(s):  
Adi Botea ◽  
Akihiro Kishimoto ◽  
Evdokia Nikolova ◽  
Stefano Braghin ◽  
Michele Berlingerio ◽  
...  
Keyword(s):  
State Space ◽  
Travel Time ◽  
Empirical Analysis ◽  
Real Life ◽  
Practical Interest ◽  
Transport Networks ◽  
Hybrid Search ◽  
Admissible Heuristics ◽  
Search Approach ◽  
Np Hardness

Multi-modal journey planning, which allows multiple types of transport within a single trip, is becoming increasingly popular, due to a strong practical interest and an increasing availability of data. In real life, transport networks feature uncertainty. Yet, most approaches assume a deterministic environment, making plans more prone to failures such as missed connections and major delays in the arrival. This paper presents an approach to computing optimal contingent plans in multi-modal journey planning. The problem is modeled as a search in an and/or state space. We describe search enhancements used on top of the AO* algorithm. Enhancements include admissible heuristics, multiple types of pruning that preserve the completeness and the optimality, and a hybrid search approach with a deterministic and a nondeterministic search. We demonstrate an NP-hardness result, with the hardness stemming from the dynamically changing distributions of the travel time random variables. We perform a detailed empirical analysis on realistic transport networks from cities such as Montpellier, Rome and Dublin. The results demonstrate the effectiveness of our algorithmic contributions, and the benefits of contingent plans as compared to standard sequential plans, when the arrival and departure times of buses are characterized by uncertainty.

Lattice Boltzmann for Turbulence Modeling

2018 ◽  
Author(s):  
Sauro Succi
Keyword(s):  
Turbulent Flows ◽  
Lattice Boltzmann ◽  
Turbulence Modeling ◽  
Real Life ◽  
Practical Interest ◽  
Reynolds Numbers ◽  
Full Resolution ◽  
To Come ◽  
Highly Turbulent Flows ◽  
Main Ideas

This chapter introduces the main ideas behind the application of LBE methods to the problem of turbulence modeling, namely the simulation of flows which contain scales of motion too small to be resolved on present-day and foreseeable future computers. Many real-life flows of practical interest exhibit Reynolds numbers far too high to be directly simulated in full resolution on present-day computers and arguably for many years to come. This raises the challenge of predicting the behavior of highly turbulent flows without directly simulating all scales of motion which take part to turbulence dynamics, but only those that fall within the computer resolution at hand.

Estimation of Path Travel Time Distributions in Stochastic Time-Varying Networks with Correlations

2021 ◽  
pp. 036119812110184
Author(s):  
Monika Filipovska ◽  
Hani S. Mahmassani ◽  
Archak Mittal
Keyword(s):  
Time Dependence ◽  
Travel Time ◽  
Correlation Structure ◽  
Time Varying ◽  
Travel Times ◽  
Trajectory Data ◽  
Time Varying Networks ◽  
Transportation Research ◽  
Search Approach ◽  
Stochastic Time

Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective, the performance of the network is experienced at the level of a path, and, as such, knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation (MCS)-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the MCS approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches, and that its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a MCS approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

scholarly journals Measuring Recurrent and Nonrecurrent Traffic Congestion

2003 ◽  
Vol 1856 (1) ◽  
pp. 118-124 ◽  
Author(s):  
Alexander Skabardonis ◽  
Pravin Varaiya ◽  
Karl F. Petty
Keyword(s):  
Los Angeles ◽  
Travel Time ◽  
San Francisco ◽  
Traffic Congestion ◽  
Real Life ◽  
Time Variability ◽  
Loop Detectors ◽  
Bay Area ◽  
Freeway Corridors ◽  
Travel Time Variability

A methodology and its application to measure total, recurrent, and nonrecurrent (incident related) delay on urban freeways are described. The methodology used data from loop detectors and calculated the average and the probability distribution of delays. Application of the methodology to two real-life freeway corridors in Los Angeles, California, and one in the San Francisco, California, Bay Area, indicated that reliable measurement of congestion also should provide measures of uncertainty in congestion. In the three applications, incident-related delay was found to be 13% to 30% of the total congestion delay during peak periods. The methodology also quantified the congestion impacts on travel time and travel time variability.

scholarly journals Solving Delete Free Planning with Relaxed Decision Diagram Based Heuristics

2020 ◽  
Vol 67 ◽  
pp. 607-651
Author(s):  
Margarita Paz Castro ◽  
Chiara Piacentini ◽  
Andre Augusto Cire ◽  
J. Christopher Beck
Keyword(s):  
Linear Programming ◽  
Empirical Analysis ◽  
State Of The Art ◽  
The State ◽  
Decision Diagrams ◽  
Binary Decision ◽  
Admissible Heuristics ◽  
The Cost ◽  
Construction Algorithms ◽  
Diagram Representation

We investigate the use of relaxed decision diagrams (DDs) for computing admissible heuristics for the cost-optimal delete-free planning (DFP) problem. Our main contributions are the introduction of two novel DD encodings for a DFP task: a multivalued decision diagram that includes the sequencing aspect of the problem and a binary decision diagram representation of its sequential relaxation. We present construction algorithms for each DD that leverage these different perspectives of the DFP task and provide theoretical and empirical analyses of the associated heuristics. We further show that relaxed DDs can be used beyond heuristic computation to extract delete-free plans, find action landmarks, and identify redundant actions. Our empirical analysis shows that while DD-based heuristics trail the state of the art, even small relaxed DDs are competitive with the linear programming heuristic for the DFP task, thus, revealing novel ways of designing admissible heuristics.

scholarly journals Travel Time Differences between Cargo Cycles and Cars in Commercial Transport Operations

2019 ◽  
Vol 2673 (8) ◽  
pp. 623-637 ◽  
Author(s):  
Johannes Gruber ◽  
Santhanakrishnan Narayanan
Keyword(s):  
Travel Time ◽  
Real Life ◽  
Time Difference ◽  
Significant Variable ◽  
Factors Affecting ◽  
Traffic Conditions ◽  
Time Performance ◽  
Trip Distance ◽  
Business Sectors ◽  
Lack Of Knowledge

Cargo cycles are gaining more interest among commercial users from different business sectors, and they compete with cars in urban commercial transport. Though many studies show the potential of cargo cycles, there is still a reluctance to deploy them. One possible reason for this is the lack of knowledge regarding their suitability in relation to travel time. Therefore, this study aims to explore cargo cycles’ travel time performance by quantifying the travel time differences between them and conventional vehicles for commercial trips. The authors compare real-life trip data from cargo cycles with Google’s routed data for cars. By doing this, the authors explore the factors affecting the travel time difference and propose a model to estimate this difference. The attributes for the model were selected keeping in mind the ease of obtaining values for the variables. Results indicate cycling trip distance to be the most significant variable. The study shows that expected travel time difference for trips with distances between 0 and 20 km (12.4 mi) ranges from -5 min (cargo cycle 5 min faster) to 40 min with a median of 6 min. This value can decrease if users take the optimal cycling route and the traffic conditions are worse for cars. Although what is an acceptable amount of travel time difference depends on the user, practitioners can be certain of the travel time difference they can expect, which enables them to assess the suitability of cargo cycles for their commercial operations.

Computing Multicriteria Shortest Paths in Stochastic Multimodal Networks Using a Memetic Algorithm

2018 ◽  
Vol 27 (07) ◽  
pp. 1860012 ◽  
Author(s):  
Omar Dib ◽  
Mohammad Dib ◽  
Alexandre Caminada
Keyword(s):  
Travel Time ◽  
Transport System ◽  
Memetic Algorithm ◽  
Human Mobility ◽  
Shortest Paths ◽  
Real Life ◽  
Hill Climbing ◽  
Deterministic Algorithms ◽  
Local Search Procedure ◽  
Multimodal Networks

The human mobility is nowadays always organized in a multimodal context. However, the transport system has become more complex. Consequently, for the sake of helping passengers, building Advanced Travelers Information Systems (ATIS) has become a certain need. Since passengers tend to consider several other criteria than the travel time, an efficient routing system should incorporate a multi-objective analysis. Besides, the transport system may behave in an uncertain manner. Integrating uncertainty into routing algorithms may thus provide more robust itineraries. The main objective of this paper is to propose a Memetic Algorithm (MA) in which a Genetic Algorithm (GA) is combined with a Hill Climbing (HC) local search procedure in order to solve the multicriteria shortest path problem in stochastic multimodal networks. As transport modes, railway, bus, tram and metro are considered. As optimization criteria, stochastic travel time, travel cost, number of transfers and walking time are taken into account. Experimental results have been assessed by solving real life itinerary problems defined on the transport network of the city of Paris and its suburbs. Results indicate that unlike classical deterministic algorithms and pure GA and HC, the proposed MA is efficient enough to be integrated within real world journey-planning systems.

scholarly journals Passenger Travel Time Reliability for Multimodal Public Transport Journeys

2019 ◽  
Vol 2673 (2) ◽  
pp. 149-160 ◽  
Author(s):  
Malvika Dixit ◽  
Ties Brands ◽  
Niels van Oort ◽  
Oded Cats ◽  
Serge Hoogendoorn
Keyword(s):  
Travel Time ◽  
Network Reliability ◽  
Real Life ◽  
Travel Time Reliability ◽  
Behavioral Models ◽  
Buffer Time ◽  
Passenger Travel ◽  
Departure Time Choice ◽  
Multiple Routes ◽  
The Impact

Urban transit networks typically consist of multiple modes and the journeys may involve a transfer within or across modes. Therefore, the passenger experience of travel time reliability is based on the whole journey experience including the transfers. Although the impact of transfers on reliability has been highlighted in the literature, the existing indicators either focus on unimodal transfers only or fail to include all components of travel time in reliability measurement. This study extends the existing “reliability buffer time” metric to transit journeys with multimodal transfers and develops a methodology to calculate it using a combination of smartcard and automatic vehicle location data. The developed methodology is applied to a real-life case study for the Amsterdam transit network consisting of bus, metro, and tram lines. By using a consistent method for all journeys in the network, reliability can be compared between different transit modes or between multiple routes for the same origin–destination pair. The developed metric can be used to study the reliability impacts of policies affecting multiple transit modes. It can also be used as an input to behavioral models such as mode, route, or departure time choice models.

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