Observed Count Data Research Articles

Responses to Centre Street Bridge Closure: Where the “Disappearing” Travelers Went

An ongoing topic of interest in urban transportation engineering is the impact of changes in road network capacity on the amount of vehicle travel in an urban area. In many cases, the debate focuses on potential increases in vehicle travel that occur with increases in road capacity—the phenomenon of “induced demand.” Some studies have also looked at the effects of reductions in roadway capacity and found that, in many of these cases, reductions in vehicle travel occur, generally confirming that a relationship exists between roadway capacity and vehicle travel. Additional information is provided on this subject in a North American context. The city of Calgary, in Alberta, Canada, is a major urban center with a population of over 850,000 and a downtown employment of over 100,000. Centre Street Bridge is a major road bridge across the Bow River that connects downtown Calgary to the residential area in the northern part of the city. The bridge carries over 34,000 vehicles per day, with heavy peak-period flows. In August 1999, the Centre Street Bridge was closed to car and truck traffic for 14 months for major repairs. A detailed study was undertaken of changes in traffic and in transit and pedestrian flows that took place in weekday travel patterns during the closure. This included both analysis of observed count data before and during the closure and an interview survey with over 1,300 car users of the Centre Street Bridge and the other bridges serving the north side of the downtown. The major findings of this study are summarized here. Particular emphasis is placed on explaining what happened to the vehicle trips that used the bridge before the closure.

Improving the resolution of gridded-hourly mobile emissions: incorporating spatial variability and handling missing data

To more accurately predict hourly running stabilized link volumes for emissions modeling, a new method was recently developed that disaggregates the period-based model link volumes into hourly volumes using observed traffic count data and multivariate multiple regression (MMR). This paper extends the MMR methodology with clustering and classification analyses to account for spatial variability and to accommodate model links that do not have matching observed traffic count data. The methodology was applied to data collected in the South Air Basin. The spatial analysis resulted in identifying five clusters (or 24-h profiles) for San Diego and two clusters for Los Angeles. The MMR models were then estimated with and without clustering. For San Diego, the disaggregated model volumes with clustering were much closer to the observed volumes than those without clustering, with the exception of the a.m. period. For most hours in Los Angeles, the predicted volumes with clustering were only slightly closer to the observed volumes than those predicted without clustering, suggesting that spatial effects are minimal in Los Angeles (i.e., that 24-h volume profiles are fairly similar throughout the region) and clustering is not necessary. Finally, two classification models, one for San Diego and one for Los Angeles were developed and tested for network link data that does not have matching observed count data. The results indicate the procedure is relatively good at predicting a cluster assignment for the unmatched location for Los Angeles but less accurate for San Diego.

Using observed traffic volumes to improve fine-grained regional emissions estimation

When translating travel demand model output to photochemical model input, period-based network assignment volumes must be converted to gridded-hourly vehicle emissions. A post-processor, such as the California Direct Travel Impact Model (DTIM2), is frequently used to disaggregate the period-based travel demand assignments to the fine grained spatial and temporal resolution required by the photochemical models. A recent theoretical enhancement proposed refining the temporal and spatial resolutions of travel demand model predictions using observed count data. This method provides a technique for disaggregating the period-based travel demand model assignments (e.g., AM peak, PM peak) into the hourly summaries required by most photochemical model ( Lin and Niemeier, 1997). In this study we present a methodological framework for applying the new theory and discuss the results of a large-scale application empirical comparison between the standard and proposed methods for estimating regional mobile emissions in Sacramento, California. The standard method produced slightly higher estimates of daily emissions (about 1%) when compared to the emissions estimated using observed count data. However, the two approaches produced hourly emissions estimates that differed by as much as 15% in some hours.

The effect of environmental factors on the suspended bacteria in the Welsh River Dee.

Water samples collected every 8 d from three sites on the Welsh River Dee over 29 months were examined chemically and morphologically and their heterotrophic activities assessed. Bacterial population estimates were regressed on 21 independent environmental variables using multiple linear regression analysis. Prediction equations calculated from 1975–1976 data accounted for 70% of the total variation from an upland site but only 40% for a lowland site. The validity of the prediction equations was examined by correlating the predicted and observed count data of 1977–1978. The correlations of plate count values were significant for the upper (r= 0.64, P < 0.001) and middle reach (r= 0.62, P < 0.01) sites but not significant for the lowland site. The direct count correlation was significant for all three sites. The permanganate value, a measure of organic matter, was the most dominant variable in the prediction equations and was shown by principal component analysis (PCA) to be linked to particulate matter and river flow. Prediction equations constructed from the PCA data accounted for less variation than the original equations but, in general, there was an agreement between the two methods of multivariate analysis. The logarithm of the heterotrophic potential (Vmax) from the lowland site was shown to be a linear function of temperature (r= 0.7, P < 0.001). A specific activity index calculated for the lowland site data gave a median value of 8.4 times 10‐10μg/h/bacterial cell and was highly sensitive to temperature change (r= 0.74, P < 0.001) whereas the median value for the upland site was 2.6 times 10‐10μg/h/bacterial cell and did not respond significantly to temperature change (r= 0.44, P > 0.05).