Biblio
Found 21 results
Filters: Author is Haris Koutsopoulos [Clear All Filters]
Automated Information Extraction From Textual Data: Application In Transit Disruption Management. In: Transportation Research Board 99th Annual Meeting. Washington, D.C.; 2020.
Capacity-Constrained Network Performance Model for Urban Rail Systems. Transportation Research Record. 2020.
Discovering Latent Activity Patterns from Transit Smart Card Data: A Spatiotemporal Topic Model. Transportation Research Part C. 2020.
Unexpected Bus Operator Absence and Extraboard Scheduling – MBTA Case Study. In: Transportation Research Board 99th Annual Meeting. Washington, D.C.; 2020.
Demand Management of Congested Public Transport Systems: A Conceptual Framework and Application Using Smart Card Data. Transportation. 2019.
Predictive decision support for real-time crowding prediction and information generation. In: Transportation Research Board 98th Annual Meeting. Washington, D.C.; 2019.
Detecting Changes in Individual Travel Behavior Patterns. In: Transportation Research Board 97th Annual Meeting. Washington, D.C.; 2018.
Detecting Pattern Changes in Individual Travel Behavior: A Bayesian Approach. Transportation Research Part B. 2018.
Discovering Latent Activity Patterns from Human Mobility. In: The 7th ACM SIGKDD International Workshop on Urban Computing (UrbComp’18).; 2018.
Individual mobility prediction using transit smart card data. Transportation Research Part C. 2018;89:19-34.
Real time transit demand prediction capturing station interactions and impact of special events. Transportation Research Part C. 2018.
Measuring Regularity of Individual Travel Patterns. IEEE Transactions on Intelligent Transportation Systems. 2017.
Mobility as A Language: Predicting Individual Mobility In Public Transportation Using N-Gram Models. In: Transportation Research Board 96th Annual Meeting.; 2017.
Clustering the Multi-week Activity Sequences of Public Transport Users. In: 95th Transportation Research Board Annual Meeting. Washington, D.C.: Transportation Research Board; 2016.
Individual-Level Trip Detection using Sparse Call Detail Record Data based on Supervised Statistical Learning. In: Transportation Research Board 95th Annual Meeting.; 2016.
Inferring patterns in the multi-week activity sequences of public transport users. Transportation Research Part C: Emerging Technologies. 2016;64:1-16.
Reducing Subway Crowding: Analysis of an Off-peak Discount Experiment in Hong Kong. Transportation Research Record: Journal of the Transportation Research Board. 2016.
Supervised Statistical Learning for Individual Level Trip Detection using Sparse Call Detail Record Data. In: 95th Transportation Research Board Annual Meeting. Washington, D.C.: Transportation Research Board; 2016.