Modeling Departure Time Choice of Car Commuters in Dhaka, Bangladesh

Dhaka, one of the fastest-growing megacities in the world, faces severe traffic congestion leading to a loss of 3.2 million business hours per day. While peak-spreading policies hold the promise to reduce the traffic congestion levels, the absence of comprehensive data sources makes it extremely challenging to develop econometric models of departure time choices for Dhaka. This motivates this paper, which develops advanced discrete choice models of departure time choice of car commuters using secondary data sources and quantifies how level-of-service attributes (e.g., travel time), socio-demographic characteristics (e.g., type of job, income, etc.), and situational constraints (e.g., schedule delay) affect their choices. The trip diary data of commuters making home-to-work and work-to-home trips by personal car/ride-hailing services (957 and 934 respectively) have been used in this regard. Given the discrepancy between the stated travel times and those extracted using the Google Directions API, a sub-model is developed first to derive more reliable estimates of travel time throughout the day. A mixed multinomial logit model and a simple multinomial logit model are developed for outbound and return trip, respectively, to capture the heterogeneity associated with different departure time choice of car commuters. Estimation results indicate that the choices are significantly affected by travel time, schedule delay, and socio-demographic factors. The influence of type of job on preferred departure time (PDT) has been estimated using two different distributions of PDT for office employees and self-employed people (Johnson’s SB distribution and truncated normal respectively). The proposed framework could be useful in other developing countries with similar data issues

Modelling residential location choices with implicit availability of alternatives

Choice set generation is a challenging aspect of disaggregate level residential location choice modelling due to the large number of candidate alternatives in the universal choice set (hundreds to hundreds of thousands). The classical Manski method (Manski, 1977) is infeasible here because of the explosion of the number of possible choice sets with the increase in the number of alternatives. Several alternative approaches have been proposed in recent years to deal with this issue, but these have limitations alongside strengths. For example, the Constrained Multinomial Logit (CMNL) model (Martínez et al., 2009) offers gains in efficiency and improvements in model fit but has weaknesses in terms of replicating the Manski model parameters. The rth-order Constrained Multinomial Logit (rCMNL) model (Paleti, 2015) performs better than the CMNL model in producing results consistent with the Manski model, but the benefits disappear when the number of alternatives in the universal choice set increases. In this study, we propose an improved CMNL model (referred to as Improved Constrained Multinomial Logit Model, ICMNL) with a higher order formulation of the CMNL penalty term that does not depend on the number of alternatives in the choice set. Therefore, it is expected to result in better model fit compared to the CMNL and the rCMNL model in cases with large universal choice sets. The performance of the ICMNL model against the CMNL and the rCMNL model is evaluated in an empirical study of residential location choices of households living in the Greater London Area. Zone level models are estimated for residential ownership and renting decisions where the number of alternatives in the universal choice set is 498 in each case. The performance of the models is examined both on the estimation sample and the holdout sample used for validation. The results of both ownership and renting models indicate that the ICMNL model performs considerably better compared to the CMNL and the rCMNL model for both the estimation and validation samples. The ICMNL model can thus help transport and urban planners in developing better prediction tools

Modelling the effects of stress on gap-acceptance decisions combining data from driving simulator and physiological sensors

Driving behaviour is an inherently complex process affected by various factors ranging from network topography, traffic conditions and vehicle features to driver characteristics like age, experience, aggressiveness and emotional state. Among these, the effects of emotional state and stress have received considerable attention in the context of crash analysis and safety research where driving behaviour has been found to be affected by drivers’ mental state/stress, cognitive workload and distraction. However, these studies are mostly based on questionnaire surveys and self-reports which can be prone to response bias and reporting/measurement errors. The analyses are also often descriptive in nature. In a parallel stream of research, advances in sensor technologies have made it possible to observe drivers’ stress through human physiological responses, e.g. heart rate, electro-dermal activity etc. However, these studies have primarily focused on detecting stress rather than quantifying or modelling its effects on driving decisions. The present paper combines these two approaches in a single framework and investigates the gap-acceptance behaviour of drivers during an intersection crossing, using data collected using a driving simulator. The participants are deliberately subjected to stress induced by time pressure, and their stress levels are measured using two physiological indicators, namely Electrodermal Activity (skin conductance) and heart rate. In addition to statistical analyses, discrete choice models are developed to link the accept-reject choices of a driver with the driver demographics, traffic conditions and stress levels. The results of the models indicate that increased stress levels significantly increase the probabilities of accepting a gap. The improvement in model fit and safety implications derived from model estimates are also discussed. The insights from the results can be used for designing appropriate intervention strategies to improve safety

Modelling lane changing behaviour in approaches to roadworks: Contrasting and combining driving simulator data with stated choice data

Drivers approaching lane closures due to roadworks tend to choose a target lane (plan) and seek suitable gaps to execute the plan (action). The plan is however latent or unobserved as the driver may or may not be able to move to the target lane due to the constraints imposed by the surrounding traffic. Hence, only the actions of the driver (as manifested by their final lane occupancies) are observed in the trajectory data. This paper analyses such mandatory lane changing behaviour in a roadworks environment in detail with data from a controlled driving simulator experiment and a simple stated preference survey with the same group of participants. While in the former drivers face similar constraints in implementing the plans as in the real world, in the simple stated choice survey the same drivers elicit their preferred target lanes without a need to put the plan into action. We contrast the findings from the two sources and also show correlations between the latent plan and stated target components in a latent class model. The results provide new insights into lane changing behaviour that may be useful for example for traffic management purposes. Furthermore, using stated choice data potentially reduces the cost of data collection for model development

Getting the best of both worlds: a framework for combining disaggregate travel survey data and aggregate mobile phone data for trip generation modelling

Traditional approaches to travel behaviour modelling primarily rely on household travel survey data, which is expensive to collect, resulting in small sample sizes and infrequent updates. Furthermore, such data is prone to reporting errors which can lead to biased parameter estimates and subsequently incorrect predictions. On the other hand, mobile phone call detail records (CDRs), which report the timestamped locations of mobile communication events, have been successfully used in the context of generating travel patterns. However, due to their anonymous nature, such records have not been widely used in developing mathematical models establishing the relationship between the observed travel behaviour and influencing factors such as the attributes of the alternatives and the decision makers. In this paper, we propose a joint modelling framework that utilises the advantages offered by both travel survey data and low-cost CDR data to optimise the prediction capacity of traditional trip generation models. In this regard, we develop a model that jointly explains the reported trips for each individual in the household survey data and ensures that the aggregated zonal trip productions are close to those derived from CDR data. This framework is tested using data from Dhaka. Bangladesh consisting of household survey data (65,419 persons in 16,750 households), mobile phone CDR data (over 600 million records generated by 6.9 million users), and aggregate census data. The model results show that the proposed framework improves the spatial and temporal transferability of the joint models over the base model which relies on household travel survey data alone. This serves as a proof-of-concept that augmenting travel survey data with mobile phone data holds significant promise for the travel behaviour modelling community, not only by saving the cost of data collection, but also improving the prediction capability of the models

Schistosomiasis and Urinary Bladder Cancer in North Western Tanzania: A Retrospective Review of 185 Patients.

Worldwide, cancers of the urinary bladder are well known to be associated with environmental chemical carcinogens such as smoking and occupational exposure to polycyclic aromatic hydrocarbons. These cancers are typically transitional cell carcinoma (urothelial carcinoma). In areas where schistosomiasis is endemic there is a high incidence of squamous cell carcinoma of the urinary bladder. Schistosomiasis causes chronic granulomatous cystitis leading to squamous metaplasia of transitional epithelium, and subsequently development of squamous cell carcinoma. The western part of Tanzania on the shores of Lake Victoria is such an endemic area. This study was done to document the burden of urinary bladder cancer associated with schistosomiasis in this region. This was a descriptive retrospective study of histologically confirmed cases of urinary bladder cancer seen at the Department of Pathology Bugando Medical Centre (BMC) over a period of 10 years. Data were retrieved from the records of the Departments of Pathology, Medical Records and Surgery. Data were analyzed by the use of contingency tables. A total of 185 patients were diagnosed with cancer of the urinary bladder during the study period, where as 90 (48.6%) were males and 95 (51.4) were females. The mean age at diagnosis was 54.3 years. Squamous cell carcinoma was the most frequent histological type (55.1%), followed by conventional transitional cell carcinoma (40.5%). Eighty three of all cancer cases (44.9%) were found to have schistosomal eggs. Schistosomiasis was commonly associated with squamous cancers compared to non squamous cancers. Most of the cancers associated with schistosomiasis had invaded the muscularis propria of the urinary bladder at the time of diagnosis (p<0.001) and such cancers were frequent below 50 years of age with a significant statistical difference (p<0.001). Poorly differentiated tumors were more frequent in females than males with a significant statistical difference (p=0.006). The majority of urinary bladder cancers seen in the Lake Region were squamous cell carcinoma associated with schistosomiasis. These cancers showed an aggressive behavior and were commonly seen in the younger age groups. Effective control of schistosomiasis in this region should significantly reduce the burden of urinary bladder cancer

Movement of environmental threats modifies the relevance of the defensive eye-blink in a spatially-tuned manner.

Subcortical reflexive motor responses are under continuous cortical control to produce the most effective behaviour. For example, the excitability of brainstem circuitry subserving the defensive hand-blink reflex (HBR), a response elicited by intense somatosensory stimuli to the wrist, depends on a number of properties of the eliciting stimulus. These include face-hand proximity, which has allowed the description of an HBR response field around the face (commonly referred to as a defensive peripersonal space, DPPS), as well as stimulus movement and probability of stimulus occurrence. However, the effect of stimulus-independent movements of objects in the environment has not been explored. Here we used virtual reality to test whether and how the HBR-derived DPPS is affected by the presence and movement of threatening objects in the environment. In two experiments conducted on 40 healthy volunteers, we observed that threatening arrows flying towards the participant result in DPPS expansion, an effect directionally-tuned towards the source of the arrows. These results indicate that the excitability of brainstem circuitry subserving the HBR is continuously adjusted, taking into account the movement of environmental objects. Such adjustments fit in a framework where the relevance of defensive actions is continually evaluated, to maximise their survival value

Blood Banking in Living Droplets

Blood banking has a broad public health impact influencing millions of lives daily. It could potentially benefit from emerging biopreservation technologies. However, although vitrification has shown advantages over traditional cryopreservation techniques, it has not been incorporated into transfusion medicine mainly due to throughput challenges. Here, we present a scalable method that can vitrify red blood cells in microdroplets. This approach enables the vitrification of large volumes of blood in a short amount of time, and makes it a viable and scalable biotechnology tool for blood cryopreservation.National Institutes of Health (U.S.) (NIH R21 EB007707)Wallace H. Coulter FoundationUnited States. Army Medical Research and Materiel Command (Acquisition Activity Cooperative Agreement RO1 A1081534)Center for Integration of Medicine and Innovative TechnologyUnited States. Army Medical Research and Materiel Command (Acquisition Activity Cooperative Agreement R21 AI087107)United States. Army. Telemedicine & Advanced Technology Research Cente

Interpretation of DAS28 and its components in the assessment of inflammatory and non-inflammatory aspects of rheumatoid arthritis

Background: DAS28 is interpreted as the inflammatory disease activity of RA. Non-inflammatory pain mechanisms can confound assessment. We aimed to examine the use of DAS28 components or DAS28-derived measures that have been published as indices of non-inflammatory pain mechanisms, to inform interpretation of disease activity. Methods: Data were used from multiple observational epidemiology studies of people with RA. Statistical characteristics of DAS28 components and derived indices were assessed using baseline and follow up data from British Society for Rheumatology Biologics Registry participants [1] commencing anti-TNF therapy (n = 10813), or [2] changing between non-biologic DMARDs (n=2992), [3] Early Rheumatoid Arthritis Network participants (n=813), and [4] participants in a cross-sectional study exploring fibromyalgia and pain thresholds (n=45). Repeatability was tested in 34 patients with active RA. Derived indices were the proportion of DAS28 attributable to patient-reported components (DAS28-P), tender-swollen difference and tender:swollen ratio. Pressure pain detection threshold (PPT) was used as an index of pain sensitisation. Results: DAS28, tender joint count, visual analogue scale, DAS28-P, tender-swollen difference and tender:swollen ratio were more strongly associated with pain, PPT and fibromyalgia status than were swollen joint count or erythrocyte sedimentation rate. DAS28-P, tender-swollen difference and tender:swollen ratio better predicted pain over 1 year than did DAS28 or its individual components. Conclusions: DAS28 is strongly associated both with inflammation and with patient-reported outcomes. DAS28-derived indices such as tender-swollen difference are associated with non-inflammatory pain mechanisms, can predict future pain and should inform how DAS28 is interpreted as an index of inflammatory disease activity in RA