Impacts of Holding Area Policies on Shared Autonomous Vehicle Operations

2021 ◽  
pp. 036119812110286
Author(s):  
Richard Twumasi-Boakye ◽  
Xiaolin Cai ◽  
Chetan Joshi ◽  
James Fishelson ◽  
Andrea Broaddus
Keyword(s):  
Service Providers ◽  
Spatial Clustering ◽  
Autonomous Vehicle ◽  
Waiting Times ◽  
Service Performance ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Temporal Variance ◽  
Spatio Temporal ◽  
Improved Performance

Shared mobility has an important role in supporting existing transportation options in cities. However, when not deployed carefully, shared services may have operational inefficiencies such as low occupancies and increased deadheading. One reason is the spatio-temporal variance in the distribution of urban trip demand, which may lead to an unbalanced fleet displaced in cities thus unable to serve requested trips. Strategically siting holding areas (depots for dispatching and relocating fleets) could help improve fleet performance. Therefore, this paper considers shared autonomous vehicle (SAV) fleet operations by modeling the impacts of different holding area policies on service performance. Modeling and comparing multiple holding area policies for tactically deploying SAVs is novel, and the insights from this paper can inform service providers on how to site holding areas for improved performance. We develop a model of SAV fleet with pooling in the City of Toronto, with 27,951 total SAV trip requests across a 16-h period. We then integrate four holding area policies estimated using different spatial clustering methods, centralized positioning, and existing taxi stands. Findings indicate that using agglomerative clustering results in superior SAV fleet performances (average passenger waiting times reduced by about 20% compared with the worst performing policy), with increased served demand and reduced deadheading. A single holding area at a high trip density location yields efficient service performance at lower fleets but struggles to serve sparse demand (producing worst results); this method may suffice for operating SAV services within a small geofence with high trip densities.

We're So Bad It's Funny - Effects of Using Humour in the Marketing Communication of Low-Quality Service Providers

2020 ◽  
Vol 4 (2-3) ◽  
pp. 84-99
Author(s):  
Ilias Danatzis ◽  
Jana Möller ◽  
Christine Mathies
Keyword(s):  
Service Quality ◽  
Service Providers ◽  
Purchase Intentions ◽  
Service Performance ◽  
Superior Performance ◽  
Service Research ◽  
Marketing Communication ◽  
Quality Service ◽  
Customer Expectations ◽  
Service Outcomes

Low-quality service providers who are unable or unwilling to compete through superior performance increasingly use humour in their marketing communication to generate positive service outcomes. Yet it remains unclear whether using humour to communicate poor service quality is indeed effective. Based on an online experiment in the context of budget hotels, this study finds that using humour to deliberately communicate poor service quality leads to higher purchase intentions and service quality evaluations by reducing both technical and functional service quality expectations. Theoretically, this study extends humour and service research by providing first empirical evidence for the viability of using humour as an effective tool for leveraging customer expectations of service quality rather than improving service performance. Managerially, these insights highlight how reducing customer expectations is an alternative strategy for attracting new customers and for achieving superior quality evaluations.

scholarly journals Quality Assessment of the Services Delivered by a Court, Based on the Perceptions of Users, Magistrates, and Court Officials

2021 ◽  
Vol 13 (2) ◽  
pp. 504
Author(s):  
Patrícia Moura e Sá ◽  
Maria João Rosa ◽  
Gonçalo Santinha ◽  
Cátia Valente
Keyword(s):  
Service Quality ◽  
Service Providers ◽  
Service Performance ◽  
Technological Equipment ◽  
Perceived Service Quality ◽  
Positive View ◽  
Depth Interview

This paper aims to measure the quality of the services delivered by a court by assessing the satisfaction of court users and service providers, i.e., magistrates and court officials. For that purpose, a case study was carried out and data were collected by means of a questionnaire based on the SERVPERF instrument, in which perceived service quality is measured, considering court users, magistrates, and court officials’ perceptions of post-service performance. One hundred and fifty-eight questionnaires were successfully returned. An in-depth interview was later conducted to the court administrator to gain a richer understanding of the results achieved and ask follow-up questions. Overall, findings revealed that court users, magistrates, and court officials clearly have a positive view of the services provided, although improvement is needed, particularly in the court’s facilities and technological equipment. The current research sheds some light on the potentialities and difficulties of assessing service quality in the judiciary and contributes to the validation of the SERVPERF instrument in this context.

App2Vec: Context-Aware Application Usage Prediction

2021 ◽  
Vol 15 (6) ◽  
pp. 1-21
Author(s):  
Huandong Wang ◽  
Yong Li ◽  
Mu Du ◽  
Zhenhui Li ◽  
Depeng Jin
Keyword(s):  
Dirichlet Process ◽  
Service Providers ◽  
State Of The Art ◽  
Representation Learning ◽  
Context Aware ◽  
Challenging Problem ◽  
Performance Gap ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Spatio Temporal

Both app developers and service providers have strong motivations to understand when and where certain apps are used by users. However, it has been a challenging problem due to the highly skewed and noisy app usage data. Moreover, apps are regarded as independent items in existing studies, which fail to capture the hidden semantics in app usage traces. In this article, we propose App2Vec, a powerful representation learning model to learn the semantic embedding of apps with the consideration of spatio-temporal context. Based on the obtained semantic embeddings, we develop a probabilistic model based on the Bayesian mixture model and Dirichlet process to capture when , where , and what semantics of apps are used to predict the future usage. We evaluate our model using two different app usage datasets, which involve over 1.7 million users and 2,000+ apps. Evaluation results show that our proposed App2Vec algorithm outperforms the state-of-the-art algorithms in app usage prediction with a performance gap of over 17.0%.

scholarly journals Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory

2021 ◽  
Vol 10 (3) ◽  
pp. 161
Author(s):  
Hao-xuan Chen ◽  
Fei Tao ◽  
Pei-long Ma ◽  
Li-na Gao ◽  
Tong Zhou
Keyword(s):  
Spatial Analysis ◽  
Spatial Clustering ◽  
Heat Index ◽  
Operating Efficiency ◽  
Clustering Methods ◽  
Clustering Method ◽  
Trajectory Mining ◽  
Density Peaks ◽  
Analysis Methods ◽  
Taxi Trajectory

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

scholarly journals Evidence for rapid avoidance of rockfish habitat under reduced quota and comprehensive at-sea monitoring in the British Columbia Pacific halibut fishery

2020 ◽  
Vol 77 (8) ◽  
pp. 1409-1420
Author(s):  
Robyn E. Forrest ◽  
Ian J. Stewart ◽  
Cole C. Monnahan ◽  
Katherine H. Bannar-Martin ◽  
Lisa C. Lacko
Keyword(s):  
British Columbia ◽  
Spatial Clustering ◽  
Clustering Methods ◽  
Quota System ◽  
Encounter Rates ◽  
System A ◽  
Pacific Halibut ◽  
Hippoglossus Stenolepis ◽  
Footprint Area ◽  
Management Changes

The British Columbia longline fishery for Pacific halibut (Hippoglossus stenolepis) has experienced important recent management changes, including the introduction of comprehensive electronic catch monitoring on all vessels; an integrated transferable quota system; a reduction in Pacific halibut quotas; and, beginning in 2016, sharp decreases in quota for yelloweye rockfish (Sebastes ruberrimus, an incidentally caught species). We describe this fishery before integration, after integration, and after the yelloweye rockfish quota reduction using spatial clustering methods to define discrete fishing opportunities. We calculate the relative utilization of these fishing opportunities and their overlap with areas with high encounter rates of yelloweye rockfish during each of the three periods. The spatial footprint (area fished) increased before integration, then decreased after integration. Each period showed shifts in utilization among four large fishing areas. Immediately after the reductions in yelloweye rockfish quota, fishing opportunities with high encounter rates of yelloweye rockfish had significantly lower utilization than areas with low encounter rates, implying rapid avoidance behaviour.

scholarly journals Cluster analysis of the results of intraoperative optical spectroscopic diagnostics In brain glioma neurosurgery

2019 ◽  
Vol 7 (4) ◽  
pp. 23-34
Author(s):  
I. A. Osmakov ◽  
T. A. Savelieva ◽  
V. B. Loschenov ◽  
S. A. Goryajnov ◽  
A. A. Potapov
Keyword(s):  
Cluster Analysis ◽  
Optical Spectroscopy ◽  
Protoporphyrin Ix ◽  
Spectroscopy Data ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Broadband Radiation ◽  
Degree Of Malignancy ◽  
Normal White Matter

The paper presents the results of a comparative study of methods of cluster analysis of optical intraoperative spectroscopy data during surgery of glial tumors with varying degree of malignancy. The analysis was carried out both for individual patients and for the entire dataset. The data were obtained using combined optical spectroscopy technique, which allowed simultaneous registration of diffuse reflectance spectra of broadband radiation in the 500–600 nm spectral range (for the analysis of tissue blood supply and the degree of hemoglobin oxygenation), fluorescence spectra of 5‑ALA induced protoporphyrin IX (Pp IX) (for analysis of the malignancy degree) and signal of diffusely reflected laser light used to excite Pp IX fluorescence (to take into account the scattering properties of tissues). To determine the threshold values of these parameters for the tumor, the infltration zone and the normal white matter, we searched for the natural clusters in the available intraoperative optical spectroscopy data and compared them with the results of the pathomorphology. It was shown that, among the considered clustering methods, EM‑algorithm and k‑means methods are optimal for the considered data set and can be used to build a decision support system (DSS) for spectroscopic intraoperative navigation in neurosurgery. Results of clustering relevant to thepathological studies were also obtained using the methods of spectral and agglomerative clustering. These methods can be used to postprocess combined spectroscopy data.

scholarly journals Coastal Change Patterns from Time Series Clustering of Permanent Laser Scan Data

2020 ◽  
Author(s):  
Mieke Kuschnerus ◽  
Roderik Lindenbergh ◽  
Sander Vos
Keyword(s):  
Time Series ◽  
Laser Scanning ◽  
Clustering Algorithm ◽  
Coastal Areas ◽  
Small Scale ◽  
Temporal Data ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Deformation Processes ◽  
Spatio Temporal

Abstract. Sandy coasts are constantly changing environments governed by complex interacting processes. Permanent laser scanning is a promising technique to monitor such coastal areas and support analysis of geomorphological deformation processes. This novel technique delivers 3D representations of a part of the coast at hourly temporal and centimetre spatial resolution and allows to observe small scale changes in elevation over extended periods of time. These observations have the potential to improve understanding and modelling of coastal deformation processes. However, to be of use to coastal researchers and coastal management, an efficient way to find and extract deformation processes from the large spatio-temporal data set is needed. In order to allow data mining in an automated way, we extract time series in elevation or range and use unsupervised learning algorithms to derive a partitioning of the observed area according to change patterns. We compare three well known clustering algorithms, k-means, agglomerative clustering and DBSCAN, and identify areas that undergo similar evolution during one month. We test if they fulfil our criteria for a suitable clustering algorithm on our exemplary data set. The three clustering methods are applied to time series of 30 epochs (during one month) extracted from a data set of daily scans covering a part of the coast at Kijkduin, the Netherlands. A small section of the beach, where a pile of sand was accumulated by a bulldozer is used to evaluate the performance of the algorithms against a ground truth. The k-means algorithm and agglomerative clustering deliver similar clusters, and both allow to identify a fixed number of dominant deformation processes in sandy coastal areas, such as sand accumulation by a bulldozer or erosion in the intertidal area. The DBSCAN algorithm finds clusters for only about 44 % of the area and turns out to be more suitable for the detection of outliers, caused for example by temporary objects on the beach. Our study provides a methodology to efficiently mine a spatio-temporal data set for predominant deformation patterns with the associated regions, where they occur.

scholarly journals Spatial and spatio-temporal analysis of Salmonella infection in dairy herds in England and Wales

2008 ◽  
Vol 137 (6) ◽  
pp. 847-857 ◽  
Author(s):  
S. E. FENTON ◽  
H. E. CLOUGH ◽  
P. J. DIGGLE ◽  
S. J. EVANS ◽  
H. C. DAVISON ◽  
...  
Keyword(s):  
Transition Probabilities ◽  
Spatial Clustering ◽  
Markov Chain Model ◽  
Temporal Analysis ◽  
Dairy Herds ◽  
Chain Model ◽  
Time Period ◽  
Spatio Temporal ◽  
Using Data ◽  
Residual Deviance

SUMMARYUsing data from a cohort study conducted by the Veterinary Laboratories Agency (VLA), evidence of spatial clustering at distances up to 30 km was found for S. Agama and S. Dublin (P values of 0·001) and borderline evidence was found for spatial clustering of S. Typhimurium (P=0·077). The evolution of infection status of study farms over time was modelled using a Markov Chain model with transition probabilities describing changes in status at each of four visits, allowing for the effect of sampling visit. The degree of geographical clustering of infection, having allowed for temporal effects, was assessed by comparing the residual deviance from a model including a measure of recent neighbourhood infection levels with one excluding this variable. The number of cases arising within a defined distance and time period of an index case was higher than expected. This provides evidence for spatial and spatio-temporal clustering, which suggests either a contagious process (e.g. through direct or indirect farm-to-farm transmission) or geographically localized environmental and/or farm factors which increase the risk of infection. The results emphasize the different epidemiology of the three Salmonella serovars investigated.

Abstract MP41: Comparisons of Built Environment Characteristics Inside and Outside of Spatial Clusters of Physical Activity and Obesity in Older U.S. Women

Circulation ◽  
2013 ◽  
Vol 127 (suppl_12) ◽  
Author(s):  
Kosuke Tamura ◽  
Robin C Puett ◽  
Jaime E Hart ◽  
Heather A Starnes ◽  
Francine Laden ◽  
...  
Keyword(s):  
Physical Activity ◽  
Built Environment ◽  
Spatial Clustering ◽  
Health Study ◽  
Clustering Methods ◽  
Spatial Clusters ◽  
Spatial Scan Statistic ◽  
Complex Picture ◽  
Intersection Density ◽  
Study Participants

Introduction: Spatial clustering methods have been applied to cancer for over a decade. These methods have been used in studies on physical activity (PA) and obesity. One recent study examined differences in built environment attributes inside and outside PA clusters. We tested two hypotheses: 1) PA and obesity would spatially cluster in older women; and 2) built environment attributes typically related to higher walkability would be found in high PA clusters, while attributes related to lower walkability would appear in high obesity clusters. Methods: We used data from 22,589 Nurses’ Health Study participants (mean age = 69.9 ± 6.8y) in California, Massachusetts, and Pennsylvania. Two outcomes were examined: meeting PA guidelines via self-reported walking (≥ 500 MET-min/week) and obesity (BMI ≥ 30.0). Objective built environment variables were created: population and intersection density, diversity of facilities, and facility density. We used a spatial scan statistic to detect clusters (i.e., areas with high or low rates) of the two outcomes. Built environment attributes were compared inside and outside clusters. Results: Six spatial clusters of PA were found in California and Massachusetts. Two obesity clusters were found in Pennsylvania. Overall there were significant differences (p<0.05) in population and intersection density, and diversity and density of facilities inside and outside clusters. In some cases, built environment attributes related to higher walkability appeared in high PA clusters, while in other PA clusters we did not find this pattern. Differences in built environment attributes inside and outside obesity clusters showed inconsistent patterns. Conclusion: Although PA and obesity clusters emerged, the comparison of built environment attributes inside and outside clusters revealed a complex picture not fully consistent with existing literature. Further examination of PA and obesity clusters in older adults should include other built environment factors that may be related to these outcomes.

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