Understanding the trip and user characteristics of the combined bicycle and transit mode

Several cities around the world are facing mobility related problems such as traffic congestion and air pollution. Although limited individually, the combination of bicycle and transit offers speed and accessibility; by complementing each other’s characteristics the bicycle and transit combination can compete with automobiles. Recognising this, several studies have investigated policies that encourage integration of these modes. However, empirical analysis of the actual users and trips of the combined mode is largely missing. This study addresses this gap by (i) reviewing empirical findings on related modes, (ii) deriving user and trip characteristics of the bicycle and transit mode in the Netherlands, and (iii) applying latent class cluster analysis to discover prototypical users based on their sociodemographic attributes. Most trips by this mode are found to be for relatively long commutes where transit is in the form of trains, and bicycle and walking are access and egress modes respectively. Furthermore, seven user groups are identified and their spatial and temporal travel behaviour is discussed. Transport authorities may use the empirical results in this study to further streamline integration of bicycle and transit for its largest users as well as to tailor policies to attract more travellers.Institute of Transport and Logistics Studies. Faculty of Economics and Business. The University of Sydne

Unsupervised approach towards analysing the public transport bunching swings formation phenomenon

We perform an analysis of public transport data from The Hague, the Netherlands, combined from three sources: static network information, automatic vehicles location and automated fare collection data. We highlight the effect of bunching swings, and show that this phenomenon can be extracted using unsupervised machine learning techniques, namely clustering. We also show the correlation between bunching rate and passenger load, and bunching probability patterns for working days and weekends. We present the approach for extracting isolated bunching swings formations (BSF) and show different cases of BSFs, some of which can persist for a considerable time. We applied our approach to the tram line 1 of The Hague, and computed and presented four different patterns of BSFs, which we name “high passenger load”, “whole route”, “evening, end of route”, “long duration”. We analyse each bunching swings formation type in detail

Urban Demand Responsive Transport in the Mobility as a Service ecosystem: its role and potential market share

Mobility as a Service (MaaS) is entering the transportation market. MaaS aims at the full integration of the existing transportation services and it offers tailored mobility packages to the user. In MaaS ecosystems, on-demand services play an important role as complement to public transport due to their flexibility. However, to date, most attention has been placed on individual on-demand services. This study focuses on Demand Responsive Transport (DRT): collective on-demand services. Using an on-line survey, we analysed the characteristics of the respondents who chose different modes of transport among their selected modes. Results find a distinctive pattern in the willingness of users to use different modes, with different levels in what could be considered as a multimodality ladder. The different rungs of it would be: 1st car (if available), 2nd public transport, 3rd DRT and 4th taxi-like services. This way, a person standing on the third rung would include car, public transport and DRT in their consideration set, but not taxi. This finding suggests that, if implemented in the right way, DRT services can attract a larger number of users than taxi-like services, especially in a MaaS ecosystem where initial barriers to try this service can be lessened.Institute of Transport and Logistics Studies. Faculty of Economics and Business. The University of Sydne

Unsupervised approach towards analysing the public transport bunching swings formation phenomenon

We perform an analysis of public transport data from The Hague, the Netherlands, combined from three sources: static network information, automatic vehicles location and automated fare collection data. We highlight the effect of bunching swings, and show that this phenomenon can be extracted using unsupervised machine learning techniques, namely clustering. We also show the correlation between bunching rate and passenger load, and bunching probability patterns for working days and weekends. We present the approach for extracting isolated bunching swings formations (BSF) and show different cases of BSFs, some of which can persist for a considerable time. We applied our approach to the tram line 1 of The Hague, and computed and presented four different patterns of BSFs, which we name “high passenger load”, “whole route”, “evening, end of route”, “long duration”. We analyse each bunching swings formation type in detail

Improving the service of E-bike sharing by demand pattern analysis: A data-driven approach

In recent years, there has been a surge in the popularity of free-floating e-bike sharing. However, the shared mobility sector is fiercely competitive demanding, efficient operations and high-quality service to cater to user expectations. We propose several data-driven methods that apply demand pattern analysis. We suggest the use of a new spatial unit (i.e., overlapping circles) to enhance the cost-efficiency and user-friendliness of e-bike sharing. Moreover, temporal clustering is employed to develop operational strategies that counter the imbalance in supply and demand in recurrent clusters. To evaluate the impact of these strategies, we introduce a framework and apply it in a case study of an e-bike sharing project in The Hague, The Netherlands. We identify 5 hourly clusters which enable reallocation strategies to alleviate the imbalance among spatial units in these clusters. The results demonstrate that the derived operational strategies improve the service significantly, with almost 1.5 times increased ridership, an approximately 20% decrease in vehicle idle time, and a decent monthly net retention rate of around 60%

F-actin-rich contractile endothelial pores prevent vascular leakage during leukocyte diapedesis through local rhoA signaling in vivo

During immune surveillance and inflammation, leukocytes exit the vasculature through transient openings in the endothelium without causing plasma leakage. However, the exact mechanisms behind this intriguing phenomenon are still unknown. Here we report that maintenance of endothelial barrier integrity during leukocyte diapedesis requires local endothelial RhoA cycling. Endothelial RhoA depletion in vitro or Rho inhibition in vivo provokes neutrophil-induced vascular leakage that manifests during the physical movement of neutrophils through the endothelial layer. Local RhoA activation initiates the formation of contractile F-actin structures that surround emigrating neutrophils. These structures that surround neutrophil-induced endothelial pores prevent plasma leakage through actomyosin-based pore confinement. Mechanistically, we found that the initiation of RhoA activity involves ICAM-1 and the Rho GEFs Ect2 and LARG. In addition, regulation of actomyosin-based endothelial pore confinement involves ROCK2b, but not ROCK1. Thus, endothelial cells assemble RhoA-controlled contractile F-actin structures around endothelial pores that prevent vascular leakage during leukocyte extravasation

F-actin-rich contractile endothelial pores prevent vascular leakage during leukocyte diapedesis through local RhoA signalling

During immune surveillance and inflammation, leukocytes exit the vasculature through transient openings in the endothelium without causing plasma leakage. However, the exact mechanisms behind this intriguing phenomenon are still unknown. Here we report that maintenance of endothelial barrier integrity during leukocyte diapedesis requires local endothelial RhoA cycling. Endothelial RhoA depletion in vitro or Rho inhibition in vivo provokes neutrophil-induced vascular leakage that manifests during the physical movement of neutrophils through the endothelial layer. Local RhoA activation initiates the formation of contractile F-actin structures that surround emigrating neutrophils. These structures that surround neutrophil-induced endothelial pores prevent plasma leakage through actomyosin-based pore confinement. Mechanistically, we found that the initiation of RhoA activity involves ICAM-1 and the Rho GEFs Ect2 and LARG. In addition, regulation of actomyosin-based endothelial pore confinement involves ROCK2b, but not ROCK1. Thus, endothelial cells assemble RhoA-controlled contractile F-actin structures around endothelial pores that prevent vascular leakage during leukocyte extravasation