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

Route Choice Behaviour: Stated Choices and Simulated Experiences

Surveys with stated choice experiments (SCE) are widely used to examine route choice behaviour in hypothetical choice contexts and to derive values of time and reliability for transport project appraisal purposes. In contrast to revealed choices, stated choices do not let participants experience (the consequences of) any attribute, which is one of the reasons why the external validity of SCE outcomes is often questioned. In this paper, we investigate the impact of simulated experiences on behaviour in a route choice context. We recruited 74 people who completed both a typical SCE and an incentive compatible driving simulator experiment (DSE), where the latter required respondents to experience the travel time of their chosen route and actually pay any toll costs associated with the choice of a tolled road. The choices are analysed via a heteroscedastic latent class model. Compared to the SCE, in the DSE, participants selected the tolled road less often, suggesting that having to pay actual money changes stated preferences. Furthermore, we found large variations in sensitivity to toll cost across participants. On the other hand, we found only minor differences in preferences towards travel time and travel time unreliability between SCE and DSE

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

A Cluster Analysis of Temporal Patterns of Travel Production in the Netherlands: Dominant within-day and day-to-day patterns and their association with Urbanization Levels

This paper explores temporal patterns in travel production using a full month of production data from traffic analysis zones (TAZ) in the (entire) Netherlands. The mentioned data is a processed aggregated derivative (due to privacy concerns) from GSM traces of a Dutch telecommunication company. This research thus also sheds light on whether such a processed data source is representative of both regular and non-regular patterns in travel production and how such data can be used for planning purposes. To this end, we construct normalized matrix (heatmap) representations of weekly hour-by-hour travel production patterns of over 1200 TAZs, which we cluster using K-means combined with deep convolutional neural networks (inception V3) to extract relevant features. A silhouette score shows that three dominant clusters of temporal patterns can be discerned (K=3). These three clusters have distinctly different within-day and day-to-day production patterns in terms of peak period intensity over different days of the week. Subsequently, a spatial analysis of these clusters reveals that the differences can be related to (easily observable) land-use features such as urbanization levels (i.e., Urban, Rural, and mixed-level). To substantiate this hypothesis and the usefulness of this clustering result, we apply an OVR-SMOTE-XGBoost ensemble classification model on the land-use features of the TAZs (i.e., to identify their cluster). The results of our clustering analysis show that given the land-use features, the overall production patterns are identifiable. Further analysis of the mixed-level areas shows a more complex relationship between temporal heterogeneity and spatial characteristics. Population density seems to impose additional uncertainty on the temporal patterns. All in all, feature selection and spatial and temporal discretization play essential roles in identifying the dominant trip production patterns. These findings are directly useful for data-driven estimation and prediction of demand time series. Furthermore, this study provides further insights into people's mobility, relevant for transportation analysis and policies

Impact of radio channel characteristics on the longitudinal behaviour of truck platoons in critical car-following situations

Truck platooning is an application of cooperative adaptive cruise control (CACC) which relies on vehicle-to-vehicle communications facilitated by vehicle ad-hoc networks. Communication uncertainties can affect the performance of a CACC controller. Previous research has not considered the full spectrum of possible car-following scenarios needed to understand how the longitudinal behaviour of truck platoons would be affected by changes in the communication network. In this paper, we investigate the impact of radio channel parameters on the string stability and collision avoidance capabilities of a CACC controller governing the longitudinal behaviour of truck platoons in a majority of critical car-following situations. We develop and use a novel, sophisticated and open-source VANET simulator OTS-Artery, which brings microscopic traffic simulation, network simulation, and psychological concepts in a single environment, for our investigations. Our results indicate that string stability and safety of truck platoons are mostly affected in car-following situations where truck platoons accelerate from the standstill to the maximum speed and decelerate from the maximum speed down to the standstill. The findings suggest that string stability can be improved by increasing transmission power and lowering receiver sensitivity. However, the safety of truck platoons seems to be sensitive to the choice of the path loos model

Advanced traffic data for dynamic OD demand estimation: the state of the art and benchmark study

In this paper, the use of advanced traffic data is discussed to contribute to the ongoing debate about their applications in dynamic OD estimation. This is done by discussing the advantages and disadvantages of traffic data with support of the findings of a benchmark study. The benchmark framework is designed to assess the performance of the dynamic OD estimation methods using different traffic data. Results show that despite the use of traffic condition data to identify traffic regime, the use of unreliable prior OD demand has a strong influence on estimation ability. The greatest estimation occurs when the prior OD demand information is aligned with the real traffic state or omitted and using information from AVI measurements to establish accurate and meaningful values of OD demand. A common feature observed by methods in this paper indicates that advanced traffic data require more research attention and new techniques to turn them into usable information.Peer ReviewedPostprint (author's final draft

Directing HIV-1 for degradation by non-target cells, using bi-specific single-chain llama antibodies

While vaccination against HIV-1 has been so far unsuccessful, recently broadly neutralizing antibodies (bNAbs) against HIV-1 envelope glycoprotein were shown to induce long-term suppression in the absence of antiretroviral therapy in patients with antibody-sensitive viral reservoirs. The requirement of neutralizing antibodies indicates that the antibody mediated removal (clearance) of HIV-1 in itself is not efficient enough in these immune compromised patients. Here we present a novel, alternative approach that is independent of a functional immune system to clear HIV-1, by capturing the virus and redirecting it to non-target cells where it is internalized and degraded. We use bispecific antibodies with domains derived from small single chain Llama antibodies (VHHs). These bind with one domain to HIV-1 envelope proteins and with the other domain direct the virus to cells expressing epidermal growth factor receptor (EGFR), a receptor that is ubiquitously expressed in the body. We show that HIV envelope proteins, virus-like particles and HIV-1 viruses (representing HIV-1 subtypes A, B and C) are efficiently recruited to EGFR, internalized and degraded in the lysosomal pathway at low nM concentrations of bispecific VHHs. This directed degradation in non-target cells may provide a clearance platform for the removal of viruses and other unwanted agents from the circulation, including toxins, and may thus provide a novel method for curing

International AIDS Society global scientific strategy: towards an HIV cure 2016

Antiretroviral therapy is not curative. Given the challenges in providing lifelong therapy to a global population of more than 35 million people living with HIV, there is intense interest in developing a cure for HIV infection. The International AIDS Society convened a group of international experts to develop a scientific strategy for research towards an HIV cure. This Perspective summarizes the group's strategy

Can passenger flow distribution be estimated solely based on network properties in public transport systems?

We present a pioneering investigation into the relation between passenger flow distribution and network properties in public transport systems. The methodology is designed in a reverse engineering fashion by utilizing passively measured passenger flow dynamics over the entire network. We quantify the properties of public transport networks using a range of centrality indicators in the topological representations of public transport networks with both infrastructure and service layers considered. All the employed indicators, which originate from complex network science, are interpreted in the context of public transport systems. Regression models are further developed to capture the correlative relation between passenger flow distribution and several centrality indicators that are selected based on the correlation analysis. The primary finding from the case study on the tram networks of The Hague and Amsterdam is that the selected network properties can indeed be used to approximate passenger flow distribution in public transport systems to a reasonable extent. Notwithstanding, no causality is implied, as the correlation may also reflect how well the supply allocation caters for the underlying demand distribution. The significance and relevance of this study stems from two aspects: (1) the unraveled relation provides a parsimonious alternative to existing passenger assignment models that require many assumptions on the basis of limited data; (2) the resulting model offers efficient quick-scan decision support capabilities that can help transport planners in tactical planning decisions