Recent research has studied the existence and the properties of a macroscopic fundamental diagram (MFD) for large urban networks. The MFD should not be universally expected as high scatter or hysteresis might appear for some type of networks, like heterogeneous networks or freeways. In this paper, we investigate if aggregated relationships can describe the performance of urban bi-modal networks with buses and cars sharing the same road infrastructure and identify how this performance is influenced by the interactions between modes and the effect of bus stops. Based on simulation data, we develop a three-dimensional vehicle MFD (3D-vMFD) relating the accumulation of cars and buses, and the total circulating vehicle flow in the network. This relation experiences low scatter and can be approximated by an exponential-family function. We also propose a parsimonious model to estimate a three-dimensional passenger MFD (3D-pMFD), which provides a different perspective of the flow characteristics in bi-modal networks, by considering that buses carry more passengers. We also show that a constant Bus–Car Unit (BCU) equivalent value cannot describe the influence of buses in the system as congestion develops. We then integrate a partitioning algorithm to cluster the network into a small number of regions with similar mode composition and level of congestion. Our results show that partitioning unveils important traffic properties of flow heterogeneity in the studied network. Interactions between buses and cars are different in the partitioned regions due to higher density of buses. Building on these results, various traffic management strategies in bi-modal multi-region urban networks can then be integrated, such as redistribution of urban space among different modes, perimeter signal control with preferential treatment of buses and bus priority.
Airports are important nodes in the air transport system, but also local sources of environmental impacts. Emissions of CO2 are among the most relevant ones because of their potential greenhouse effects. Many policies and guidelines have been identified at national and world level to reduce such kind of impacts. In this paper, a Transport Carbon Footprint methodology has been set to identify Unit Carbon Footprints (UCFs) linked to some identified emission macro-sources – i.e., land vehicles, on-ground aircraft, airport handling and terminal equipment – to compute the contribution of the single macro-source to the total amount of CO2. Particularly, UCFs due to transport activities have been defined according to some relevant transport variables. The computation of UCF values for a given airport allows computing both the contribution of each macro-source and also evaluating the effectiveness of transport-related actions aiming at reducing the carbon impact. The methodology has been applied to the airport of Bologna, in Northern Italy, and its UCF values for the identified macro-sources have been computed.
Had the report based estimates on more current historic data—e.g., VMT trends for 2003-13, which grew at one-fifth the USDOT’s 1995-2010 estimate—the cost estimates would have dropped by tens of billions more, reducing pressure on budgets while freeing up funds to bring the existing system to a state of good repair.
Freeway analysis procedures in the widely used Highway Capacity Manual (HCM) include the input of a driver population factor (Fp), which allows the analyst to adjust the demand depending on the familiarity of drivers with the roadway. This adjustment is based on the assumption that unfamiliar drivers will drive at slower speeds with longer headways and that higher capacity would therefore be required. However, little research supports the use of the Fp, and the HCM cautions against the use of Fp unless the analyst is fairly certain the traffic stream is actually unfamiliar with the roadway. As an experiment, three bottlenecks in California were selected and analyzed during the weekday peaks and weekend afternoons in periods during which the traffic stream was likely to be nonlocal. The results showed that the changes in flow were minor at all three locations. Further research with additional sites and an increased awareness of the definition of familiarity will be required to confirm the results from this research.
The design of suburban communities encourages car dependency and discourages walking, characteristics that have been implicated in the rise of obesity. Walkability measures have been developed to capture these features of urban built environments. Our objective was to examine the individual and combined associations of residential density and the presence of walkable destinations, two of the most commonly used and potentially modifiable components of walkability measures, with transportation, overweight, obesity, and diabetes. We examined associations between a previously published walkability measure and transportation behaviors and health outcomes in Toronto, Canada, a city of 2.6 million people in 2011. Data sources included the Canada census, a transportation survey, a national health survey and a validated administrative diabetes database. We depicted interactions between residential density and the availability of walkable destinations graphically and examined them statistically using general linear modeling. Individuals living in more walkable areas were more than twice as likely to walk, bicycle or use public transit and were significantly less likely to drive or own a vehicle compared with those living in less walkable areas. Individuals in less walkable areas were up to one-third more likely to be obese or to have diabetes. Residential density and the availability of walkable destinations were each significantly associated with transportation and health outcomes. The combination of high levels of both measures was associated with the highest levels of walking or bicycling (p<0.0001) and public transit use (p<0.0026) and the lowest levels of automobile trips (p<0.0001), and diabetes prevalence (p<0.0001). We conclude that both residential density and the availability of walkable destinations are good measures of urban walkability and can be recommended for use by policy-makers, planners and public health officials. In our setting, the combination of both factors provided additional explanatory power.
Escalators are an essential mode of public transportation that enable people to travel vertically within a facility at a continuous, high flow rate. Despite the importance of the role of escalators in many facilities, little systematic analysis of the capacity of escalators has been conducted within the field of transportation engineering. A method is presented to calculate the practical capacity of escalators with a simulation based on pedestrian behavioral rules. The capacity of an escalator is defined traditionally only as a function of speed with speed-capacity curves defined by manufacturers or found in empirical studies. These methods do not consider pedestrian behavioral patterns and preferences such as following distance, passing aggressiveness, and other local factors. A rule-based model provides the flexibility to analyze conditions in various public facilities and to answer hypothetical research questions. Three major findings are reported. First, the practical capacity of escalators in commercial facilities such as shopping malls is significantly lower than the maximum capacity in a commuter facility such as a transit station, at only 20% to 40% of what is generally reported by manufacturers to provide for freedom of movement and pedestrian comfort. Second, the model shows that prohibition of walking on escalators can stream-line operations in emergency scenarios because it reduces variability in the system and increases flow, particularly during peak periods. Finally, contrary to some claims in the literature, uphill flow on escalators operates at a lower capacity than does downhill flow because of the presence of a "facial ellipse," the region directly in front of a pedestrian's face.
This study examines the impacts of the built environment measures based on two geographic scales, i.e., traffic analysis zone and one quarter-mile buffer on individual mode choice in the Houston metropolitan area. It is confirmed that they have significant impacts on mode choice in varying degrees. The models including the buffer-based measures are more reasonable than those with conventional zone-based variables for both home-based work and other trips. Finally, the elasticity estimates suggest the built environments are undervalued in the conventional transportation practices. Both land use and transport pricing measures should be considered complementary to control the demand for driving.
Potential advantages of global positioning systems (GPS) in collecting travel behavior data have been discussed in several publications and evidenced in many recent studies. Most applications depend on GPS information only. However, transportation mode detection that relies only on GPS information may be erroneous due to variance in device performance and settings, and the environment in which measurements are made. Accelerometers, being used mainly for identifying peoples’ physical activities, may offer new opportunities as these devices record data independent of exterior contexts. The purpose of this paper is therefore to examine the merits of employing accelerometer data in combination with GPS data in transportation mode identification. Three approaches (GPS data only, accelerometer data only and a combination of both accelerometer and GPS data) are examined. A Bayesian Belief Network model is used to infer transportation modes and activity episodes simultaneously. Results show that the use of accelerometer data can make a substantial contribution to successful imputation of transportation mode. The accelerometer only approach outperforms the GPS only approach in terms of the predictive accuracy. The approach which combines GPS and accelerometer data yields the best performance.
In the spirit of Open Access Week, here's an interesting article from an open access journal - The Journal of Transport and Land Use. Go check it out and peruse the articles. No need to depend on your institution's sibscription because it's free to the public! (Thanks open access!)
This paper presents a methodology to investigate the link between bicycle activity and built environment, road and transit network characteristics, and bicycle facilities while also accounting for spatial autocorrelation between intersections. The methodology includes the normalization of manual cyclist counts to average seasonal daily volumes (ASDV), taking into account temporal variations and using hourly, daily, and monthly expansion factors obtained from automatic bicycle count data. To correct for weather conditions, two approaches were used. In the first approach, a relative weather ridership model was generated using the automatic bicycle count and weather data. In the second approach, weather variables were introduced directly into the model. For each approach, the effects of built environment, road and transit characteristics, and bicycle facilities on cyclist volumes were determined. It was found that employment, schools, metro stations, bus stops, parks, land mix, mean income, bicycle facility type (bicycle lanes and cycle tracks), length of bicycle facilities, average street length, and presence of parking entrances were associated with bicycle activity. From these, it was found that the main factors associated with bicycle activity were land-use mix, cycle track presence, and employment density. For instance, intersections with cycle tracks have on average 61 percent more cyclists than intersections without. An increase of 10 percent in land-use mix or employment density would cause an increase of 8 percent or 5.3 percent, respectively, in bicycle flows. The methods and results proposed in this research are helpful for planning bicycle facilities and analyzing cyclist safety. Limitations and future work are discussed at the end of this paper.