Using Deficit Function to Determine the Minimum Fleet Size of an Autonomous Modular Public Transit System

2020 ◽  
Vol 2674 (11) ◽  
pp. 532-541 ◽  
Author(s):  
Tao Liu ◽  
Avishai (Avi) Ceder ◽  
Andreas Rau
Keyword(s):  
Autonomous Vehicles ◽  
Large Scale ◽  
Public Transit ◽  
Programming Model ◽  
Real Life ◽  
Single Line ◽  
Continuum Approximation ◽  
Transit System ◽  
Fleet Size ◽  
Minimum Number

Emerging technologies, such as connected and autonomous vehicles, electric vehicles, and information and communication, are surrounding us at an ever-increasing pace, which, together with the concept of shared mobility, have great potential to transform existing public transit (PT) systems into far more user-oriented, system-optimal, smart, and sustainable new PT systems with increased service connectivity, synchronization, and better, more satisfactory user experiences. This work analyses such a new PT system comprised of autonomous modular PT (AMPT) vehicles. In this analysis, one of the most challenging tasks is to accurately estimate the minimum number of vehicle modules, that is, its minimum fleet size (MFS), required to perform a set of scheduled services. The solution of the MFS problem of a single-line AMPT system is based on a graphical method, adapted from the deficit function (DF) theory. The traditional DF model has been extended to accommodate the definitions of an AMPT system. Some numerical examples are provided to illustrate the mathematical formulations. The limitations of traditional continuum approximation models and the equivalence between the extended DF model and an integer programming model are also provided. The extended DF model was applied, as a case study, to a single line of an AMPT system, the dynamic autonomous road transit (DART) system in Singapore. The results show that the extended DF model is effective in solving the MFS problem and has the potential to be applied to solving real-life MFS problems of large-scale, multi-line and multi-terminal AMPT systems.

Optimal Operational Strategies for Multiple Bus Lines Considering Passengers’ Preferences

2020 ◽  
Vol 2674 (5) ◽  
pp. 572-586
Author(s):  
Chunyan Tang ◽  
Avishai Ceder ◽  
Ying-En Ge ◽  
Na Wu
Keyword(s):  
Public Transit ◽  
Real Life ◽  
System Efficiency ◽  
Sample Problem ◽  
Transit System ◽  
Operational Strategies ◽  
Passenger Demand ◽  
Single Strategy ◽  
Operational Integration

A public transit system with multiple fixed bus lines faces non-uniform fluctuating passenger demand, both spatial and temporal. This non-uniformity warrants the use of public transit operational strategies to achieve efficiency. This study proposes a methodology for optimizing the operational integration of multiple bus lines to address the spatial non-uniformity of passenger demand by applying five operational strategies: full-route operation, short turn, limited stop, deadheading, and a mixture of either two or three of the latter three strategies. The operational strategies to be developed improve the efficiency of bus lines and accommodate the observed passenger demand in the most favorable manner, that is, through the consideration of passengers’ preferences with the objective of the minimum resulting cost. The methodology is first applied to a sample problem, and then to a real-life case study of multiple bus lines in Dalian, China. The results obtained demonstrate that the effectiveness of combined strategies is higher than that of any single strategy. In the real-life bus line case, a combination of strategies without considering deadheading trips saves four vehicles in comparison with the full-route operation scenario. The anticipated number of vehicles is further reduced by three by the introduction of the deadheading trip strategy, resulting in greater public transit system efficiency.

scholarly journals Living Lab as an Ecosystem for Development, Demonstration and Assessment of Autonomous Mobility Solutions

2019 ◽  
Vol 11 (15) ◽  
pp. 4095 ◽  
Author(s):  
Andreja Pucihar ◽  
Iztok Zajc ◽  
Radovan Sernec ◽  
Gregor Lenart
Keyword(s):  
Autonomous Vehicles ◽  
Large Scale ◽  
Business Models ◽  
Real Life ◽  
Road Accidents ◽  
Innovation Ecosystem ◽  
Living Lab ◽  
Industry Environment ◽  
New Business ◽  
Autonomous Mobility

Autonomous vehicles (AV) have the potential to disrupt the entire transport industry. AV may bring many opportunities as for example reduction of road accidents, less congestion on the roads, and a lower number of vehicles that are better utilized. Full AV also brings new social element as they enable mobility for all. In addition, the use of digital technologies in combination with AV introduces new business models in transportation, where the lines between car ownership, rental, and lease modes are more and more blurred. To explore the potential of AV in a smart city context, the AV Living Lab was created on the premises of BTC City in Ljubljana, Slovenia, in 2017. The AV Living lab was created to test and to learn about real-life solutions for implementation of AV. The underlying concept is BTC City as a Living lab innovation ecosystem, where the latest advanced technologies, business models, and services are tested with real users, real cars, on real roads over the real interactions in a cross-industry environment. In this paper, we describe the AV Living Lab concept and provide details of a specific use case—a large-scale pilot demonstration of AV and future mobility solutions. During the event, users participated in a survey and expressed their attitudes towards autonomous mobility. The results offer the first insights into the readiness of citizens for AV implementation and directs future actions needed for faster adoption of AV and future mobility solutions.

scholarly journals Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1106
Author(s):  
S. Bhaskaran ◽  
Raja Marappan ◽  
B. Santhi
Keyword(s):  
Large Scale ◽  
Real Life ◽  
Optimization Methods ◽  
Tuning Parameter ◽  
Computational Time ◽  
Data Set ◽  
Significant Difference ◽  
Minimum Number ◽  
Tremendous Amount ◽  
The Given

Nowadays, because of the tremendous amount of information that humans and machines produce every day, it has become increasingly hard to choose the more relevant content across a broad range of choices. This research focuses on the design of two different intelligent optimization methods using Artificial Intelligence and Machine Learning for real-life applications that are used to improve the process of generation of recommenders. In the first method, the modified cluster based intelligent collaborative filtering is applied with the sequential clustering that operates on the values of dataset, user′s neighborhood set, and the size of the recommendation list. This strategy splits the given data set into different subsets or clusters and the recommendation list is extracted from each group for constructing the better recommendation list. In the second method, the specific features-based customized recommender that works in the training and recommendation steps by applying the split and conquer strategy on the problem datasets, which are clustered into a minimum number of clusters and the better recommendation list, is created among all the clusters. This strategy automatically tunes the tuning parameter λ that serves the role of supervised learning in generating the better recommendation list for the large datasets. The quality of the proposed recommenders for some of the large scale datasets is improved compared to some of the well-known existing methods. The proposed methods work well when λ = 0.5 with the size of the recommendation list, |L| = 30 and the size of the neighborhood, |S| < 30. For a large value of |S|, the significant difference of the root mean square error becomes smaller in the proposed methods. For large scale datasets, simulation of the proposed methods when varying the user sizes and when the user size exceeds 500, the experimental results show that better values of the metrics are obtained and the proposed method 2 performs better than proposed method 1. The significant differences are obtained in these methods because the structure of computation of the methods depends on the number of user attributes, λ, the number of bipartite graph edges, and |L|. The better values of the (Precision, Recall) metrics obtained with size as 3000 for the large scale Book-Crossing dataset in the proposed methods are (0.0004, 0.0042) and (0.0004, 0.0046) respectively. The average computational time of the proposed methods takes <10 seconds for the large scale datasets and yields better performance compared to the well-known existing methods.

scholarly journals Optimization of Transit Scheduling Combined with Short-Turn Service Based on Real-Time Passenger Flow

2020 ◽  
Author(s):  
Ailing Huang ◽  
Yijing Miao ◽  
Jiarui Li
Keyword(s):  
Real Time ◽  
Public Transit ◽  
Load Ratio ◽  
Total System ◽  
Transit System ◽  
Fleet Size ◽  
Passenger Flow ◽  
Vehicle Data ◽  
Transit Scheduling ◽  
Working Day

In view of a series of problems, such as unable to meet the needs of passengers, high full load ratio or waste of carrying capacity on unbalanced passenger flow sections caused by the all-stop fleet scheduling in the urban public transit system, this paper proposed a bus combination scheduling strategy with considering short-turn service based on the imbalance coefficient of passenger flow and a method to determine the turning back point. A combined dispatching optimization model is established with the objective function of minimizing the total system cost which includes the waiting time cost of passengers, the congestion feeling cost and the operation cost of public transit enterprises. The headways of short-turn and all-stop scheme are optimized by the combined scheduling model, and the solution method is proposed. Taking Beijing No. A bus line as an empirical analysis object, the real-time passenger flow and vehicle data in a working day are collected and analyzed, and the optimized scheme of short-turn service combination scheduling is obtained. The results show that compared with the traditional all-stop fleet scheduling, the optimized short-turn service combination scheduling can reduce the fleet size by 4.9% and effectively improve the operation efficiency and system benefits.

scholarly journals Enabling In-Depot Automated Routing and Recharging Scheduling for Automated Electric Bus Transit Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lei Wang ◽  
Wanjing Ma ◽  
Ling Wang ◽  
Yongli Ren ◽  
Chunhui Yu
Keyword(s):  
Autonomous Vehicles ◽  
Job Shop ◽  
Optimal Solution ◽  
Transit System ◽  
Transit Systems ◽  
Bus Transit ◽  
Electric Bus ◽  
Minimum Number ◽  
First Come First Serve ◽  
High Level

The bus transit system is promising to enable electric and autonomous vehicles for massive urban mobility, which relies on high-level automation and efficient resource management. Besides the on-road automation, the in-depot automated scheduling for battery recharging has not been adequately studied yet. This paper presents an integrated in-depot routing and recharging scheduling (IDRRS) problem, which is modeled as a constraint programming (CP) problem with Boolean satisfiability conditions (SAT). The model is converted to a flexible job-shop problem (FJSP) and is feasible to be solved by a CP-SAT solver for the optimal solution or feasible solutions with acceptable performance. This paper also presents a case study in Shanghai and compares the results from the FJSP model and the first-come first-serve (FCFS) method. The result demonstrates the allocation of routes and chargers under multiple scenarios with different numbers of chargers. The results show that the FJSP model shortens the delay and increases the time conservation for future rounds of operation than FCFS, while FCFS presents the simplicity of programming and better computational efficiency. The multiple random input test suggests that the proposed approach can decide the minimum number of chargers for stochastic charging requests. The proposed method can conserve the investment by increasing the utilization of automated recharging devices, improving vehicles’ in-depot efficiency.

Strategic vehicle fleet management–a joint solution of make-or-buy, composition and replacement problems

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Adam Redmer
Keyword(s):  
Management Strategy ◽  
Large Scale ◽  
Real Life ◽  
Optimization Methods ◽  
Solution Procedure ◽  
Fleet Management ◽  
Content Type ◽  
Fleet Size ◽  
Real Life Data ◽  
Management Problems

PurposeThe purpose of this paper is to develop an original model and a solution procedure for solving jointly three main strategic fleet management problems (fleet composition, replacement and make-or-buy), taking into account interdependencies between them.Design/methodology/approachThe three main strategic fleet management problems were analyzed in detail to identify interdependencies between them, mathematically modeled in terms of integer nonlinear programing (INLP) and solved using evolutionary based method of a solver compatible with a spreadsheet.FindingsThere are no optimization methods combining the analyzed problems, but it is possible to mathematically model them jointly and solve together using a solver compatible with a spreadsheet obtaining a solution/fleet management strategy answering the questions: Keep currently exploited vehicles in a fleet or remove them? If keep, how often to replace them? If remove then when? How many perspective/new vehicles, of what types, brand new or used ones and when should be put into a fleet? The relatively large scale instance of problem (50 vehicles) was solved based on a real-life data. The obtained results occurred to be better/cheaper by 10% than the two reference solutions – random and do-nothing ones.Originality/valueThe methodology of developing optimal fleet management strategy by solving jointly three main strategic fleet management problems is proposed allowing for the reduction of the fleet exploitation costs by adjusting fleet size, types of exploited vehicles and their exploitation periods.

An artificial intelligent approach to the bicycle repositioning problems

2017 ◽  
Vol 34 (1) ◽  
pp. 145-163 ◽  
Author(s):  
Peng-Sheng You ◽  
Pei-Ju Lee ◽  
Yi-Chih Hsieh
Keyword(s):  
Vehicle Routing ◽  
Large Scale ◽  
Programming Model ◽  
Phase 1 ◽  
Mixed Integer ◽  
Problem Size ◽  
Solution Quality ◽  
Content Type ◽  
Fleet Size ◽  
Imbalance Problem

Purpose Many bike rental organizations permit customers to pick-up bikes from one bike station and return them at a different one. However, this service may result in bike imbalance, as bikes may accumulate in stations with low demand. To overcome the imbalance problem, this paper aims to develop a decision model to minimize the total costs of unmet demand and empty bike transport by determining bike fleet size, deployments and the vehicle routing schedule for bike transports. Design/methodology/approach This paper developed a constrained mixed-integer programming model to deal with this bike imbalance problem. The proposed model belongs to the non-deterministic polynomial-time (NP)-hard problem. This paper developed a two-phase heuristic approach to solve the model. In Phase 1, the approach determines fleet size, deployment level and the number of satisfied demands. In Phase 2, the approach determines the routing schedule for bike transfers. Findings Computational results show the following results that the proposed approach performs better than General Algebraic Modeling System (GAMS) in terms of solution quality, regardless of problem size. The objective values and the fleet size of rental bikes allocated increase as the number of rental stations increases. The cost of transportation is not directly proportional to the number of bike stations. Originality/value The authors provide an integrated model to simultaneously deal with the problems of fleet sizing, empty-resource repositioning and vehicle routing for bike transfer in multiple-station systems, and they also present an algorithm that can be applied to large-scale problems which cannot be solved by the well-known commercial software, GAMS/CPLEX.

scholarly journals OPTIMIZING LIMITED-STOP BUS SERVICES ALONG A PUBLIC TRANSIT CORRIDOR WITH A DIFFERENTIAL FARE STRUCTURE

Transport ◽  
2019 ◽  
Vol 34 (4) ◽  
pp. 476-489 ◽  
Author(s):  
Chunyan Tang ◽  
Ying-En Ge ◽  
William H. K. Lam
Keyword(s):  
Social Cost ◽  
Travel Demand ◽  
Public Transit ◽  
Social Equity ◽  
Heavy Load ◽  
Transit System ◽  
Fleet Size ◽  
Bus Service ◽  
Total Social Cost ◽  
Bus Services

Limited-stop bus services are a highly efficient way to release more potential of the public transit system to meet travel demand, especially under constraints on vehicle fleet size and transportation infrastructure. This work first proposes a visualized fare table for the design of limited-stop bus services along a public transit corridor, along which many lines of public transit carry a heavy load of demand back and forth every working day. Based on this proposed fare table, a set of fare strategies and desired aims of fare policy, a differentiated fare structure is established to improve social equity and increase revenue. The nature of the structure can help travellers understand how to be charged between their origins and destinations (e.g. flat, time-based, stop-based or quality-based pricing) and then plan their trips efficiently. Secondly, a model is formulated to minimize the total social cost in designing a fixed demand limited-stop bus service system with a differentiated fare structure. Thirdly, numerical results are carried out with sensitivity analysis within three scenarios of differentiated fare structures. It is found that a differentiated fare structure has a great effect on passenger path choice behaviour and resulting optimal design of bus services. An attractive feature of this differentiated fare structure is that it could not only enhance the operator’s revenue and social equity but also reduce passenger transfers and social cost.

scholarly journals On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment

2017 ◽  
Vol 114 (3) ◽  
pp. 462-467 ◽  
Author(s):  
Javier Alonso-Mora ◽  
Samitha Samaranayake ◽  
Alex Wallar ◽  
Emilio Frazzoli ◽  
Daniela Rus
Keyword(s):  
New York ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
High Capacity ◽  
Fleet Size ◽  
Ride Sharing ◽  
Large Numbers ◽  
General Mathematical Model ◽  
Travel Delay

Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics). We present a more general mathematical model for real-time high-capacity ride-sharing that (i) scales to large numbers of passengers and trips and (ii) dynamically generates optimal routes with respect to online demand and vehicle locations. The algorithm starts from a greedy assignment and improves it through a constrained optimization, quickly returning solutions of good quality and converging to the optimal assignment over time. We quantify experimentally the tradeoff between fleet size, capacity, waiting time, travel delay, and operational costs for low- to medium-capacity vehicles, such as taxis and van shuttles. The algorithm is validated with ∼3 million rides extracted from the New York City taxicab public dataset. Our experimental study considers ride-sharing with rider capacity of up to 10 simultaneous passengers per vehicle. The algorithm applies to fleets of autonomous vehicles and also incorporates rebalancing of idling vehicles to areas of high demand. This framework is general and can be used for many real-time multivehicle, multitask assignment problems.

scholarly journals Neural methods for effective, efficient, and exposure-aware information retrieval

2021 ◽  
Vol 55 (1) ◽  
pp. 1-2
Author(s):  
Bhaskar Mitra
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Large Scale ◽  
Web Search ◽  
Real Life ◽  
Inverted Index ◽  
Information Need ◽  
Product Model ◽  
Performance Improvements ◽  
Deep Model

Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents---or short passages---in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms---such as a person's name or a product model number---not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections---such as the document index of a commercial Web search engine---containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks. We ground our contributions with a detailed survey of the growing body of neural IR literature [Mitra and Craswell, 2018]. Our key contribution towards improving the effectiveness of deep ranking models is developing the Duet principle [Mitra et al., 2017] which emphasizes the importance of incorporating evidence based on both patterns of exact term matches and similarities between learned latent representations of query and document. To efficiently retrieve from large collections, we develop a framework to incorporate query term independence [Mitra et al., 2019] into any arbitrary deep model that enables large-scale precomputation and the use of inverted index for fast retrieval. In the context of stochastic ranking, we further develop optimization strategies for exposure-based objectives [Diaz et al., 2020]. Finally, this dissertation also summarizes our contributions towards benchmarking neural IR models in the presence of large training datasets [Craswell et al., 2019] and explores the application of neural methods to other IR tasks, such as query auto-completion.

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