Modeling Escalator Capacity

Escalators are an under appreciated component of many public transportation systems. Can you imagine using the Dupont Circle Metro station without them? 

A recent paper from the Transportation Research Record examines the role of escalators in public transit systems. "Modeling the Practical Capacity of Escalators: A Behavioral Approach to Pedestrian Simulation" by Peter Kauffmann and Shinya Kikuchi looks at how escalators affect pedestrian behavior and public transit. 

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.

The full paper can be found online here. 


Built Environment Impacts on Individual Mode Choice

Downtown Houston

The built environment has an impact on mode choice. It's a topic ripe for study. In "Built Environment Impacts on Individual Mode Choice: An Empirical Study of the Houston-Galveston Metropolitan Area" by Jae-Su Lee, Jin Nam & Sam-Su Lee examines the built environment and mode choice for the city of Houston, publised in the most recent issues of the International Journal of Sustainable Transportation.

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.

You can read the whole paper here

Transportation mode recognition using GPS and accelerometer data


One of the big problems for smartphone travel diary apps is automatic mode detection. The split between walking and not is pretty easy, as is cycling, but what about separating cars from rail? Apps like Moves just dubs it "transport", but that doesn't help much with travel behavior research. A new paper in Transportarion Researc Part C examines using accelerometers and GPS to detect mode. Tao Feng and Harry J.P. Timmermans from Eindhoven University of Technology present their research in, "Transportation mode recognition using GPS and accelerometer data"

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.

The full article can be found here

Open Access Article: Spatial modeling of bicycle activity at signalized intersections

Biking at Grand/Halsted/Milwaukee (3 of 4)

This week is Open Access Week. What's Open Access? Here is a not very brief overview by Peter Suber. UC Berkeley also has an Open Access Initiative to help open up your research and data. 

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!)

In "Spatial modeling of bicycle activity at signalized intersections", Jillian Strauss and Luis F Miranda-Moreno look at the built-environment and cycling. 

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.

The full article can be found here

Great California Shake Out! Are you ready?

Oct. 18 1989 Cypruss Overpass Collapse

Today is the Great California Shake Out, a statewide earthquake drill.  Do you know what to do when the Big One comes? 

Ever since the 1989 earthquake and its effects on transportation infrastructure, such as the collapse of the Cypress Freeway (which of course was replaced) or the structural failure of a portion of the San Francisco-Oakland Bay Bridge, there has been considerable research on seismic safety and stability. Are aerial BART stations safe? What are the optimal risk-based maintenance procedures to earthquake safety for bridges and highways?

Of course the Los Angeles region has its own concerns about the Big One, such as the disruption of freight logistics for the mega-region. How will the region's highways be affected? There were lots of lessons learned from the 1994 Northridge earthquake, which help inform projections for future risks.

If you want more research about earthquakes and California transportation, of course turn to TRID. For today, think about what you'd do when the Big One hits and how you prepare for such an event. 

Stay safe. 

Ant colony routing for Freeways

ants all in it

Do we drive like ants? Researchers from TU Delft's Center for Systems and Control use an ant routing algorithm for freeways. "Ant Colony Routing algorithm for freeway networks" by Zhe Cong, Bart De Schutter and Robert Babuška, explore this topic. 

Dynamic traffic routing refers to the process of (re)directing vehicles at junctions in a traffic network according to the evolving traffic conditions. The traffic management center can determine desired routes for drivers in order to optimize the performance of the traffic network by dynamic traffic routing. However, a traffic network may have thousands of links and nodes, resulting in a large-scale and computationally complex non-linear, non-convex optimization problem. To solve this problem, Ant Colony Optimization (ACO) is chosen as the optimization method in this paper because of its powerful optimization heuristic for combinatorial optimization problems. ACO is implemented online to determine the control signal – i.e., the splitting rates at each node. However, using standard ACO for traffic routing is characterized by four main disadvantages: 1. traffic flows for different origins and destinations cannot be distinguished; 2. all ants may converge to one route, causing congestion; 3. constraints cannot be taken into account; and 4. neither can dynamic link costs. These problems are addressed by adopting a novel ACO algorithm with stench pheromone and with colored ants, called Ant Colony Routing (ACR). Using the stench pheromone, the ACR algorithm can distribute the vehicles over the traffic network with less or no traffic congestion, as well as reduce the number of vehicles near some sensitive zones, such as hospitals and schools. With colored ants, the traffic flows for multiple origins and destinations can be represented. The proposed approach is also implemented in a simulation-based case study in the Walcheren area, the Netherlands, illustrating the effectiveness of the approach.

You can find the full article here

Wetter Stau: Examining Extreme Weather and Traffic Congestion in Germany.

Wenig Schnee - viel Chaos

Extreme weather events, such as blizzards or heavy rains, cause traffic congestion. A new article in the Journal of Advanced Transportation looks at the relationship in Germany. In "A study of the influence of severe environmental conditions on common traffic congestion features," Hubert Rehborn and Micha Koller use German traffic data to study the relationship on the Autobahn. 

On the basis of real traffic and environmental data measured on German freeways, we studied common features of traffic congestion under the influence of severe weather conditions. We have found that traffic features [J] and [S] defining traffic phases “wide moving jam” (J) and “synchronized flow” (S) in Kerner's three-phase theory are indeed common spatiotemporal traffic features. The quantitative parameters for both traffic phases [S] and [J] were investigated in a comparison of “ideal” weather conditions (good visibility and no precipitation) and severe weather situations (icy road, wind, precipitation, etc.). We showed spatiotemporal congested patterns in several space–time diagrams based on the Automatic Tracking of Moving Jams/Forecasting of Traffic Objects (ASDA/FOTO) model reconstruction for roadside detectors. A statistical study of traffic phase [J] parameters was presented, showing the average values and standard deviation of the quantities. Similarities and differences were analyzed, and some consequences for vehicular applications were discussed to cope with severe weather conditions.

The full article can be found here

Urban Gridlock

Chicago Gridlock

Gridlock is a fact of life in urban areas. Why is that? A new study explores the characteristics of urban gridlock, to better understand the condition and ways to ease congestion. From Transportation Research Part C: Emerging Techonologies, "Urban network gridlock: Theory, characteristics, and dynamics" by Hani S. Mahmassani, Meead Saberi, and Ali Zockaie tackles the issue. 

This study explores the limiting properties of network-wide traffic flow relations under heavily congested conditions in a large-scale complex urban street network; these limiting conditions are emulated in the context of dynamic traffic assignment (DTA) experiments on an actual large network. The primary objectives are to characterize gridlock and understand its dynamics. This study addresses a gap in the literature with regard to the existence of exit flow and recovery period. The one-dimensional theoretical Network Fundamental Diagram (NFD) only represents steady-state behavior and holds only when the inputs change slowly in time and traffic is distributed homogenously in space. Also, it does not describe the hysteretic behavior of the network traffic when a gridlock forms or when network recovers. Thus, a model is proposed to reproduce hysteresis and gridlock when homogeneity and steady-state conditions do not hold. It is conjectured that the network average flow can be approximated as a non-linear function of network average density and variation in link densities. The proposed model is calibrated for the Chicago Central Business District (CBD) network. We also show that complex urban networks with multiple route choices, similar to the idealized network tested previously in the literature, tend to jam at a range of densities that are smaller than the theoretical average network jam density. Also it is demonstrated that networks tend to gridlock in many different ways with different configurations. This study examines how mobility of urban street networks could be improved by managing vehicle accumulation and redistributing network traffic via strategies such as demand management and disseminating real-time traveler information (adaptive driving). This study thus defines and explores some key characteristics and dynamics of urban street network gridlocks including gridlock formation, propagation, recovery, size, etc.

The full paper can be found here.

Rebalancing and Bikeshare

The mythical #DivvyRed

Is this the summer of Bikeshare? Divvy Bikes in Chicago launched last month. CitiBikes in New York City launched around Memorial Day. Any time now Bay Area Bike Share will be launching in San Francisco and on then Peninsula. 

The issue of having bikes where people want them is a perennial issue for bikeshare systems. "Rebalancing" is the act of moving inventory around to match demand and travel patterns. This map provides realtime visualizations of the demand of bikeshare systems around the world. Researchers are working on solving the rebalancing problem

A new article from EURO Journal on Transportation and Logistics works to develop a model for rebalancing. "Static repositioning in a bike-sharing system: models and solution approaches" by Tal Raviv, Michal Tzur, and Iris A. Forma, looks at how rebalancing or repositioning can help bikeshare systems.

Bike-sharing systems allow people to rent a bicycle at one of many automatic rental stations scattered around the city, use them for a short journey and return them at any station in the city. A crucial factor for the success of a bike-sharing system is its ability to meet the fluctuating demand for bicycles and for vacant lockers at each station. This is achieved by means of a repositioning operation, which consists of removing bicycles from some stations and transferring them to other stations, using a dedicated fleet of trucks. Operating such a fleet in a large bike-sharing system is an intricate problem consisting of decisions regarding the routes that the vehicles should follow and the number of bicycles that should be removed or placed at each station on each visit of the vehicles. In this paper, we present our modeling approach to the problem that generalizes existing routing models in the literature. This is done by introducing a unique convex objective function as well as time-related considerations. We present two mixed integer linear program formulations, discuss the assumptions associated with each, strengthen them by several valid inequalities and dominance rules, and compare their performances through an extensive numerical study. The results indicate that one of the formulations is very effective in obtaining high quality solutions to real life instances of the problem consisting of up to 104 stations and two vehicles. Finally, we draw insights on the characteristics of good solutions.

The full paper can be found here

Crowding in transit: How does it effects on riders, operations and demand.

SCRTD Crowded Bus Stop RTD_1131_13

Crowded bus stops and subway stations, which beget crowded buses and trains, are a part of riding transit. ITS Berkeley researchers are exploring how this effects rider attitudes

A new article from Transportation Research Part A: Policy and Practice examines this issue. In "Crowding in public transport systems: Effects on users, operation and implications for the estimation of demand," researchers from Chile and Australia look at the effects of crowding on speed, waiting times, travel time reliability, and route choice. 

The effects of high passenger density at bus stops, at rail stations, inside buses and trains are diverse. This paper examines the multiple dimensions of passenger crowding related to public transport demand, supply and operations, including effects on operating speed, waiting time, travel time reliability, passengers’ wellbeing, valuation of waiting and in-vehicle time savings, route and bus choice, and optimal levels of frequency, vehicle size and fare. Secondly, crowding externalities are estimated for rail and bus services in Sydney, in order to show the impact of crowding on the estimated value of in-vehicle time savings and demand prediction. Using Multinomial Logit (MNL) and Error Components (EC) models, we show that alternative assumptions concerning the threshold load factor that triggers a crowding externality effect do have an influence on the value of travel time (VTTS) for low occupancy levels (all passengers sitting); however, for high occupancy levels, alternative crowding models estimate similar VTTS. Importantly, if demand for a public transport service is estimated without explicit consideration of crowding as a source of disutility for passengers, demand will be overestimated if the service is designed to have a number of standees beyond a threshold, as analytically shown using a MNL choice model. More research is needed to explore if these findings hold with more complex choice models and in other contexts.

The full article can be found here

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