Inhaled particle counts on bicycle commute routes of low and high proximity to motorised traffic

Frequent exposure to ultrafine particles (UFP) is associated with detrimental effects on cardiopulmonary function and health. UFP dose and therefore the associated health risk are a factor of exposure frequency, duration, and magnitude of (therefore also proximity to) a UFP emission source. Bicycle commuters using on-road routes during peak traffic times are sharing a microenvironment with high levels of motorised traffic, a major UFP emission source. Inhaled particle counts were measured along popular pre-identified bicycle commute route alterations of low (LOW) and high (HIGH) motorised traffic to the same inner-city destination at peak commute traffic times. During commute, real-time particle number concentration (PNC; mostly in the UFP range) and particle diameter (PD), heart and respiratory rate, geographical location, and meteorological variables were measured. To determine inhaled particle counts, ventilation rate was calculated from heart-rate-ventilation associations, produced from periodic exercise testing. Total mean PNC of LOW (compared to HIGH) was reduced (1.56 x e4 ± 0.38 x e4 versus 3.06 x e4 ± 0.53 x e4 ppcc; p = 0.012). Total estimated ventilation rate did not vary significantly between LOW and HIGH (43 ± 5 versus 46 ± 9 L•min; p = 0.136); however, due to total mean PNC, accumulated inhaled particle counts were 48% lower in LOW, compared to HIGH (7.6 x e8 ± 1.5 x e8 versus 14.6 x e8 ± 1.8 x e8; p = 0.003). For bicycle commuting at peak morning commute times, inhaled particle counts and therefore cardiopulmonary health risk may be substantially reduced by decreasing exposure to motorised traffic, which should be considered by both bicycle commuters and urban planners

Freeway Origin Destination Matrices: Not as Simple as They Seem

Travel demand can be elegantly represented using an Origin-Destination (OD) matrix. The link counts observed on the network are produced by the underlying travel demand. One could use these counts to reconstruct the OD matrix. An offline approach to estimate a static OD matrix over the peak period for freeway sections using these counts is proposed in this research. Almost all the offline methods use linear models to approximate the relationship between the on-ramp and off-ramp counts. Previous work indicates that the use of a traffic flow model embedded in a search routine performs better than these linear models. In this research that approach is enhanced using a microscopic traffic simulator, AIMSUN, and a gradient based optimization routine, MINOS, interfaced to estimate an OD matrix. This approach is an application of the Prediction Error Minimization (PEM) method. The problem is non-linear and non-smooth, and the optimization routine finds multiple local minima, but cannot guarantee a global minima. However, with a number of starting seed matrices, an OD matrix with a good fit in terms of reproducing traffic counts can be estimated. The dominance of the mainline counts in the OD estimation and an identifiability issue is indicated from the experiments. The quality of the estimates improves as the specification error, introduced due to the discrepancy between the traffic flow model and the real world process that generates the on-ramp and off-ramp counts, reduces.travel demand, OD estimation, simulation, optimization

Operational Evidence of Changing Travel Patterns

This paper utilizes a traffic counts database covering a ten year period (1976-1985) to identify travel trends for Montgomery County, a suburb of Washington D.C. Generally, travel behavior is analyzed using person based travel survey data. The use of traffic counts to understand travel behavior is a relatively new approach. Unlike household surveys, which are typically characterized by respondent and sample bias, and require special effort for their collection, traffic counts are routinely collected by Departments of Transportation and provide the best available measure of observed traffic volumes. The study provides fresh evidence to support some of the earlier findings: an increase in lateral commuting as a share of travel, changes in work and non-work trip proportions, and increase in peak spreading. An interesting result in this paper relates to a more pronounced directionality in radial as compared with lateral trips. The relative symmetry of traffic flows along lateral routes compared with radial routes results in better utilization of the suburban road network. Non-work trips emerge as the more elastic trips, shifting to off-peak hours with an increase in congestion. .

A Primal-Dual Algorithm for Link Dependent Origin Destination Matrix Estimation

Origin-Destination Matrix (ODM) estimation is a classical problem in transport engineering aiming to recover flows from every Origin to every Destination from measured traffic counts and a priori model information. In addition to traffic counts, the present contribution takes advantage of probe trajectories, whose capture is made possible by new measurement technologies. It extends the concept of ODM to that of Link dependent ODM (LODM), keeping the information about the flow distribution on links and containing inherently the ODM assignment. Further, an original formulation of LODM estimation, from traffic counts and probe trajectories is presented as an optimisation problem, where the functional to be minimized consists of five convex functions, each modelling a constraint or property of the transport problem: consistency with traffic counts, consistency with sampled probe trajectories, consistency with traffic conservation (Kirchhoff's law), similarity of flows having close origins and destinations, positivity of traffic flows. A primal-dual algorithm is devised to minimize the designed functional, as the corresponding objective functions are not necessarily differentiable. A case study, on a simulated network and traffic, validates the feasibility of the procedure and details its benefits for the estimation of an LODM matching real-network constraints and observations

Measurement of noise events in road traffic streams: initial results from a simulation study

A key question for road traffic noise management is whether prediction of human response to noise, including sleep quality, could be improved over the use of conventional energy equivalent, or percentile, measures, by accounting for noise events in road traffic streams. This paper reports initial results from a noise-events investigation into event-based indicators over an exhaustive set of traffic flow, traffic composition, and propagation distance, conditions in unshielded locations in proximity to roadways. We simulate the time-varying noise level histories at various distances from roadways using a dynamic micro-traffic model and a distribution of sound power levels of individual vehicles. We then develop a comprehensive set of noise event indicators, extrapolated from those suggested in the literature, and use them to count noise events in these simulated time histories. We report the noise-event algorithms that produce realistic, and reliable, counts of noise events for one-hour measurement periods, then reduce redundancy in the indicator set by suggesting a small number of representative event indicators. Later work will report the traffic composition and distance conditions under which noise event measures provide information uncorrelated with conventional road traffic noise indicators — and which thus may prove useful as supplementary indicators to energy-equivalent measures for road traffic noise

Road-traffic pollution and asthma – using modelled exposure assessment for routine public health surveillance

Asthma is a common disease and appears to be increasing in prevalence. There is evidence linking air pollution, including that from road-traffic, with asthma. Road traffic is also on the increase. Routine surveillance of the impact of road-traffic pollution on asthma, and other diseases, would be useful in informing local and national government policy in terms of managing the environmental health risk. Several methods for exposure assessment have been used in studies examining the association between asthma and road traffic pollution. These include comparing asthma prevalence in areas designated as high and low pollution areas, using distance from main roads as a proxy for exposure to road traffic pollution, using traffic counts to estimate exposure, using vehicular miles travelled and using modelling techniques. Although there are limitations to all these methods, the modelling approach has the advantage of incorporating several variables and may be used for prospective health impact assessment. The modelling approach is already in routine use in the United Kingdom in support of the government's strategy for air quality management. Combining information from such models with routinely collected health data would form the basis of a routine public health surveillance system. Such a system would facilitate prospective health impact assessment, enabling policy decisions concerned with road-traffic to be made with knowledge of the potential implications. It would also allow systematic monitoring of the health impacts when the policy decisions and plans have been implemented

Development of origin–destination matrices using mobile phone call data

In this research, we propose a methodology to develop OD matrices using mobile phone Call Detail Records (CDR) and limited traffic counts. CDR, which consist of time stamped tower locations with caller IDs, are analyzed first and trips occurring within certain time windows are used to generate tower-to-tower transient OD matrices for different time periods. These are then associated with corresponding nodes of the traffic network and converted to node-to-node transient OD matrices. The actual OD matrices are derived by scaling up these node-to-node transient OD matrices. An optimization based approach, in conjunction with a microscopic traffic simulation platform, is used to determine the scaling factors that result best matches with the observed traffic counts. The methodology is demonstrated using CDR from 2.87 million users of Dhaka, Bangladesh over a month and traffic counts from 13 key locations over 3 days of that month. The applicability of the methodology is supported by a validation study