Experimental Study for Optimizing Pedestrian Flows at Bottlenecks of Subway Stations

In subway stations, bottlenecks are the narrowed areas that reduce pedestrian flows in channels. Because pedestrians at bottlenecks are forced to dense together, bottlenecks decrease flow efficiency and pedestrians’ transfer comfort and may trigger serious crowd disasters such as trampling. This study used pedestrian experiments to investigate the methods of optimizing pedestrian traffic at bottlenecks of subway stations. Three optimization measures were proposed and evaluated by analyzing the characteristics of pedestrian flows, including efficiency, smoothness, and security. In this paper, setting the rear sides of the bottleneck entrance as straight and surface funnel shapes is called straight funnel shape and surface funnel shape, respectively. Setting a column at a bottleneck is called the column obstacle. The results showed that when efficiency or security come first, a column on the left is recommended; when comfort comes first, a concave funnel is recommended; when comprehensiveness is prioritized, a column on the left is recommended. Moreover, the larger the volume, the optimization is more obvious. Although many  bottlenecks cannot be prevented when subway stations are constructed, the proposed optimization measures may help ease their adverse effects by improving facility efficiency, smoothness, and security, and by providing recommendations for designing and managing subway stations.</p

Pilgrim crowd dynamics

Among the steady progression of disasters worldwide lie the numerous instances of fatality where crowds gather. The scale of these is particularly high at the Hajj in Makkah, where there are exceptionally high numbers of pedestrians in a number of confined areas and, depending on the time of year, all in searing heat. In order to reduce the likelihood of repetition in the future, the present thesis involved firstly determining the characteristics of the pedestrians attending the Hajj, and then collecting speed, flow and density data by observing them walking along one of the busiest roads between the Holy Mosque and the other holy sites, Ajyad Street. These were analyzed against various models from the literature including those of Greenshield, Weidmann and Greenberg, and it was found that none of these fitted convincingly, mostly because pilgrims do not walk at the maximum speeds that the crowd density allows. This thesis proposes the use instead of a maximum possible speed model based on a linear relationship between speed and density i.e. $u$ ≤ 1.75 (1 - $k$ /5.47) where $u$ is speed (m/s) and $k$ is density (people/m$^2$). It then goes on demonstrate with a simulation model that an increase of 50% in traffic with the current layout would result in severe overcrowding. This however could be avoided relatively easily by a particular combination of changing the directions of flow and the geometry of the road

Transportation planning via location-based social networking data : exploring many-to-many connections

textToday’s metropolitan areas see changes in populations and land development occurring at faster rates than transportation planning can be updated. This dissertation explores the use of a new dataset from the location-based social networking spectrum to analyze origin-destination travel demand within Austin, TX. A detailed exploration of the proposed data source is conducted to determine its overall capabilities with respect to the Austin area demographics. A new methodology is proposed for the creation of origin-destination matrices using a peer-to-peer modeling structure. This methodology is compared against a previously examined and more traditional approach, the doubly-constrained gravity model, to understand the capabilities of both models with various friction functions. Each method is examined within the constructs of the study area’s existing origin-destination matrix by examining the coincidence ratios, mean errors, mean absolute errors, frequency ratios, swap ratios, trip length distributions, zonal trip generation and attraction heat maps, and zonal origin-destination flow patterns. Through multiple measures, this dissertation provides initial interpretations of the robust Foursquare data collected for the Austin area. Based upon the data analytics performed, the Foursquare data source is shown to be capable of providing immensely detailed spatial-temporal data that can be utilized as a supplementary data source to traditional transportation planning data collection methods or in conjunction with other data sources, such as social networking platforms. The examination of the proposed peer-to-peer methodology presented within this dissertation provides a first look at the potential of many-to-many modeling for transportation planning. The peer-to-peer model was found to be superior to the doubly-constrained gravity model with respect to intrazonal trips. Furthermore, the peer-to-peer model was found to better estimate productions, attractions, and zone to zone movements when a linear function was used for long trips, and was computationally more proficient for all models examined.Civil, Architectural, and Environmental Engineerin