scholarly journals The Impact of Fleet Coordination on Taxi Operations

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Claudio Ruch ◽  
Sebastian Hörl ◽  
Joel Gächter ◽  
Jan Hakenberg
Keyword(s):  
San Francisco ◽  
Operating Mode ◽  
Service Level ◽  
Demand Systems ◽  
High Fidelity ◽  
Data Sets ◽  
On Demand ◽  
Mode Share ◽  
Self Driving Cars ◽  
The Impact

On-demand mobility has existed for more than 100 years in the form of taxi systems. Comparatively recently, ride-hailing schemes have also grown to a significant mode share. Most types of such one-way mobility-on-demand systems allow drivers taking independent decisions. These systems are not or only partially coordinated. In a different operating mode, all decisions are coordinated by the operator, allowing for the optimization of certain metrics. Such a coordinated operation is also implied if human-driven vehicles are replaced by self-driving cars. This work quantifies the service quality and efficiency improvements resulting from the coordination of taxi fleets. Results based on high-fidelity transportation simulations and data sets of existing taxi systems are presented for the cities of San Francisco, Chicago, and Zurich. They show that fleet coordination can strongly improve the efficiency and service level of existing systems. Depending on the operator and the city’s preferences, empty vehicle distance driven and fleet sizes could be substantially reduced, or the wait times could be reduced while maintaining the current fleet sizes. The study provides clear evidence that full fleet coordination should be implemented in existing mobility-on-demand systems, even before the availability of self-driving cars.

Towards Sustainable Liveable City: Management Operations of Shared Autonomous Cargo-Bike Fleets

2021 ◽  
Vol 1 (3) ◽  
pp. 505-532
Author(s):  
Imen Haj Salah ◽  
Vasu Dev Mukku ◽  
Malte Kania ◽  
Tom Assmann
Keyword(s):  
Public Transportation ◽  
Service Level ◽  
City Management ◽  
Sustainable Mobility ◽  
Fleet Size ◽  
On Demand ◽  
Mobility Service ◽  
Cargo Bike ◽  
The Impact

Finding a sustainable mobility solution for the future is one of the most competitive challenges in the logistics and mobility sector at present. Policymakers, researchers, and companies are working intensively to provide novel options that are environmentally friendly and sustainable. While autonomous car-sharing services have been introduced as a very promising solution, an innovative alternative is arising: the use of self-driving bikes. Shared autonomous cargo-bike fleets are likely to increase the livability and sustainability of the city, as the use of cargo-bikes in an on-demand mobility service can replace the use of cars for short-distance trips and enhance connectivity to public transportation. However, more research is needed to develop this new concept. In this paper, we investigate different rebalancing strategies for an on-demand, shared-use, self-driving cargo-bikes service (OSABS). We simulate a case study of the system in the inner city of Magdeburg using AnyLogic. The simulation model allows us to evaluate the impact of rebalancing on service level, idle mileage, and energy consumption. We conclude that the best proactive rebalancing strategy for our case study is to relocate bikes only between neighboring regions. We also acknowledge the importance of bike relocation to improve service efficiency and reduce fleet size.

Bayesian inference with overlapping data: methodology for reliability estimation of multi-state on-demand systems

2012 ◽  
Vol 226 (3) ◽  
pp. 283-294 ◽  
Author(s):  
C Jacksonn ◽  
A Mosleh
Keyword(s):  
Likelihood Function ◽  
Reliability Estimation ◽  
Demand Systems ◽  
Data Sets ◽  
Equivalent Representation ◽  
On Demand ◽  
Analysis Methodology ◽  
Overlapping Data ◽  
Level Data ◽  
System States

A Bayesian system reliability analysis methodology for multiple overlapping higher level data sets within complex multi-state on-demand systems is presented in this paper. Data sets are overlapping if they are drawn from the same process at the same time, with reliability data from sensors attached to a system being a prime example. Treating overlapping data as non-overlapping loses or incorrectly infers information. The approach generated in this paper is able to incorporate overlapping data from multi-state on-demand systems with a detailed understanding of the system logic represented using fault trees, reliability block diagrams or another equivalent representation. Structure functions of the system at relevant sensor locations (developed from the system logic) in terms of component states are used in conjunction with the probability of all possible system states (or all possible state vectors) to generate the likelihood function of overlapping evidence. This forms the basis of the likelihood function used in the Bayesian analysis of the overlapping data sets.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

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