In binary networks, the focus is on whether or not a connection between two nodes exists. However, not all links (or nodes) are created equal, particularly when it comes to transportation networks. When we know about the presence of a link as well as the strength of that link, it is called a valued network. For instance when traveling from A to B in a street network, there is usually discontinuity in street type. In other words, one might move from a local street to a collector road to an arterial road and then back to a collector before reaching their destination. While engineers know this sort of differentiation as functional classification, it is also referred to as hierarchy.
Hierarchy, which is embedded in many natural and societal systems such as biologic cells and the Internet, is a common transportation complexity that requires a more complicated topological analysis (Tomko, Winter, & Claramunt, 2008). Typical topological measures such as Degree or Betweenness can be useful in helping understand network hierarchy, particularly in tree-like networks; however, such measures would fail to properly distinguish between streets in a gridded street network. In the above version of Metropolis’ street network, the major streets are represented by thicker lines and easily discerned, even in a gridded network (Fleischer, 1941). Using the basic set of topological metrics, we would have no idea that 8th Street is functionally different from 7th Street or F Street from D Street. These metrics fail to consider attributes – such as urban design, number of lanes, active transportation infrastructure, adjacent land uses, and speed – beyond network structure and would not necessarily be able to distinguish such streets.
Working with hierarchical networks often involves dividing networks in multiple layers or tiers. Measurements of heterogeneity have also become common proxies for characterizing hierarchy. To identify heterogeneity among street segments, researchers have used entropy measures as well as discontinuity measures (Xie, 2005). Discontinuity, for example, does not necessarily denote a disconnected network; rather, the reference is to the discontinuity in moving from one street type to another. If we sum the number of times a traveler goes from one type of street to another while traveling along a shortest path route, we find the trip discontinuity. Dividing that number by the length of the trip gives us the relative discontinuity (Parthasarathi, 2011). Other simplistic hierarchy measures calculate the relative percentage of a particular type of road. For instance, we might divide the number or length of arterials by the total number or length of roads to find the relative percent arterials (Parthasarathi, 2011).
Interestingly, it is not uncommon for large-scale transportation models to delete streets on the lower end of the hierarchical spectrum (i.e. local streets) for the sake of computational efficiency. Yet, removing such streets creates a bias against more connected networks because less connected networks typically need to be supported by major streets with more capacity than would be needed in more connected networks (Bern & Marshall, 2012). Some topological researchers – where the focus should be on understanding the full network – unfortunately reach the same conclusion: “urban streets demonstrate a hierarchical structure in the sense that a majority is trivial, while a minority is vital” (Jiang, 2009). If we only care about vehicle traffic flow, such statements may be true. However, my previous street network research confirms that understanding the full network holds the key to pushing toward improved safety, increased active transportation, and better environmental and health outcomes (Bern & Marshall, 2013; Marshall & Garrick, 2009, 2010a, 2010b, 2012).
Urban planners and engineers have long been interested in measuring street connectivity and typically do so with relatively simple measures such as the link to node ratio (called the Beta Index in the Transport Geography field), which divides the total number of links (i.e. road segments between intersections) by the total number of nodes (i.e. intersections including dead ends). In the above image, the connected network has link to node ratio of 1.6 while the dendritic network’s link to node ratio is 1.0 (a link to node ration of 1.4 is typically considered a well-connected street network).
The connected node ratio divides the number of connected nodes (i.e. nodes that are not dead ends) by total number of nodes (Handy, Paterson, & Butler, 2003). The networks above have a connected node ratio of 1.0 and 0.6, respectively. The underlying intent is distinguish between well-connected or gridded street networks and dendritic, treelike networks – as highlighted in the figure above – in researching relevant issues such as travel behavior, road safety, VMT, and public health outcomes.
Topology takes a slightly different approach to understanding this issue. The Meshedness Coefficient, for instance, measures connectivity by looking at the number of cycles in the network with respect to the maximum number of cycles (a cycle is a closed path that begins and ends at the same node with no fewer than three links). A Meshedness Coefficient of 0 represents full tree structure (i.e. no cycles), and 1 represents complete connectivity (i.e. every node is directly connected to every other node, which is not feasible in a large surface transportation network) (Buhl et al., 2006). In non-planar networks, this measure is also known in Transport Geography as the Alpha Index. The Alpha for the connected network above is 0.4 and for the dendritic network, it is just 0.03. For large networks, Beta and Alpha are highly correlated.
Xie and Levinson (2007) developed another useful metric called Treeness. Instead of counting the number of cycles, Treeness is instead calculated by dividing the length of street segments not within a cycle by the total length of street segments. The Treeness measure also provides a value between 0 and 1, but in this case, the higher number represents a more treelike or dendritic network (Xie & Levinson, 2007).
Networks with good overall connectivity are called integrated networks. Networks with low connectivity are called fractured networks (although fractured networks can still be comprised of connected components). Again, these measures relate to issues of resilience. When a single node failure can significantly erode network functionality, the system is fragile. The image below shows a fallen tree in Lake Oswego, OR that cut off more than 50 families from the outside world (or more specifically, the cars of more than 50 households were trapped) (Florip, 2010). If only that network had a little less Treeness.
When we have nodes or links with high Betweenness values, it is often because our network is split into various sub-groups that can be called clusters. Clusters tend to have their own unique set of properties, so it is useful to be able to identify clusters quantitatively.
While there are a growing number of clustering algorithms, the basic idea behind them is to capture the degree to which nodes cluster. The Clustering coefficient, for instance, represents how likely is it that two connected nodes are part of a larger group of highly connected nodes. It can be calculated by dividing number of actual connections between the neighbors of a node (i.e. the nodes directly connected to the node in question) by the number of possible connections between these same neighboring nodes. For instance in the image above, the red node is the node of interest, and it has a Degree of 4. Those 4 neighboring nodes make 4 actual connections (i.e. the black lines in the figure on the right) but have 6 possible connections (i.e. the black lines plus the red dashed lines). Thus, the Clustering coefficient for the red node is 4 divided by 6 or 0.67.
The value represented by the Clustering coefficient ranges from 0 (i.e. no clustering) to 1 (i.e. complete clustering). If we are interested in the amount of clustering for an entire network, we average the Clustering coefficients for all of the nodes. Clustering tends to be higher in real-world networks than in random networks. So when a network becomes more centralized (i.e. a small percentage of nodes have high connectivity), the overall topology becomes more differentiated and clusters begin to emerge.
Other related terms include component and clique. When a given sub-group of nodes is also highly connected, that is called a component. When the nodes in a component have few connections to other nodes outside of the component, that is a clique. Understanding clusters, components, and cliques in networks can be useful because they can hold more influence over behavior than overall network structure (Neal, 2013). Imagine, for instance, a New Urbanist neighborhood with great street connectivity set into a city with poor overall street connectivity. Analyzing network structure for the overall city might lead us to one conclusion; yet, we could find very different outcomes in the New Urbanist neighborhood. While factors such as land use, street design, and demographics influence transportation-related outcomes as well, the concept of clustering holds value for those interested in truly understanding transportation networks.
Degree is often good for measuring local circumstance, but adequately characterizing centrality is a bit more complicated. When trying to figure out centrality in terms of how connected and influential a node or link is, it is useful to get a sense of relative network flow through a particular node or link.
Betweenness measures attempt to capture this relative flow by quantifying the number of times a node or link is on a shortest path between two other nodes. The first step would be to calculate the shortest path between every origin and every destination. Next, we count the number of times that a particular node or link shows up on a shortest path. The resulting number represents the relative role of a node or link as a connector between clusters of nodes or links. In the above street network, the intersection highlighted in red must be included in over half of the shortest paths. We call this count Betweenness, which is essentially an attempt to quantify how necessary a node or link is to get from one side of the network to the other. The Panama Canal, for instance, is a key maritime link connecting the Atlantic and Pacific Oceans. Without it, ships would have to route around Cape Horn at the southernmost tip of Chile or through the Straits of Magellan. For a ship traveling from New York to San Francisco, the Panama Canal – due to its high Betweenness value – cuts more than 7,500 miles from the journey. In terms of other transportation issues, Betweenness usually relates to metrics such as accessibility and traffic congestion.
In addition to revealing relative importance, Betweenness also indicates how irreplaceable a node or link may be to a network. In other words, what happens if we remove a certain node or link from the network? Very high betweenness values can indicate a critical connection between various groups of nodes or links. In some cases, this represents a vulnerability where we would want to add redundancies to the network.
In transportation networks, if we assume all travelers take the shortest path and treat each traveler as having a unique origin and destination, Betweenness is the same as the flow (number of travelers) on the link. We call this Flow-weighted Betweenness.
Centrality measures help gauge the overall importance of a node. In other words, how connected and how influential is a node within the overall network?
One of the simplest measures of centrality is Degree, which measures the number of connections between a node and all other nodes. For instance if we are considering a street network with intersections as nodes, a nodal Degree of 4 would indicate a typical 4-way intersection.
The image above depicts a rendition of the Metropolis street network with a Degree value shown at each intersection and a 4-way intersection highlighted in red (Fleischer, 1941). When we focus on what is happening at one particular node, it is called the ego network (in that we are looking at the network from the perspective of a single node while ignoring all nodes not directly connected, which can be deemed a bit narcissistic). The entire network can be called the complete, whole, or global network. So if we want an overall Degree measure, we can calculate Average Degree, which is the average number of connections for all the nodes within the overall network. When the Average Degree exceeds 1, every node has at least one connection, on average. When the Average Degree approaches log(n), where n equals the number of nodes in the network, every node starts to become accessible from every other node (Neal, 2013). For the Metropolis network, there are 78 nodes with an Average Degree of 3.4.
Analyzing Degree measures for a complete network also entails generating a Degree Distribution, which literally equates to the plotting the frequency of each Degree for all the nodes as shown in the image below for the Metropolis street network. The idea is to try to capture the relative differences in connectivity between the nodes in order to gain a sense of network structure. For instance, every node in a homogenous network would have the exact same number of connections and not much of a distribution. A more centralized network might have one node with a high Degree value and all other nodes with low Degree values.
Networks play a role in nearly all facets of our daily lives, particularly when it comes to transportation. Even within the transportation realm lays a relatively broad range of different network types such as air networks, freight networks, bus networks, and train networks (not to mention the accompanying power and communications networks). We also have the ubiquitous street network, which not only defines how you get around a city, but it provides the form upon which our cities are built and experienced. Cities around the world that are praised for having good street networks come in many different configurations ranging from the medieval patterns of cities like Prague and Florence, to the organic networks of Boston and London, and the planned grids of Washington D.C. and Savannah, Georgia. But how do researchers begin to understand and quantify the differences in such networks?
The primary scientific field involved with the study of shapes and networks is called topology. Based in mathematics, topology is a subfield of geometry that allows one to transform a network via stretching or bending. Under a topological view, a network that has been stretched like a clock in a Salvador Dali painting would be congruent with the original, unstretched network. This would not be the case in conventional Euclidean geometry where differences in size or angle cannot be ignored. The transportation sector typically models networks as a series of nodes and links (Levinson & Krizek, 2008). The node (or vertex) is the fundamental building block of the model; links (or edges) are not independent entities but rather are represented as connections between two nodes. Connectivity – and the overall structure of the network that emerges from that connectivity – is what topology is all about. In other words, topology cares less about the properties of the objects themselves and more about how they come together.
For instance if we look at the topology of a light rail network, the stations would typically be considered the nodes and the rail lines would be the links. In this case, the stations are the actors in the network, and the rail lines represent the relationships between the actors (Neal, 2013). Those relationships – and more specifically, those connections – embody what is important. Taking a similar approach with a street network, we might identify the intersections as the nodes and the street segments as the links as shown in the network based on an early version of Metropolis above (Fleischer, 1941). For most street networks, however, the street segments are just as important as the intersections, if not more so. The ‘space syntax’ approach takes the opposite (or ‘dual’) approach with street networks: the nodes represent the streets, and the lines between the nodes (i.e. the links or edges) are present when two streets are connected at an intersection, as shown below using the same Metropolis network (Jiang, 2007).
Initial theories related to topology trace back to 1736 with Leonhard Euler and his paper on the Seven Bridges of Königsberg. Graph theory based topological measures first debuted in the late 1940s (Bavelas, 1948) and were initially developed in papers analyzing social networks (Freeman, 1979) and the political landscape (Krackhardt, 1990). Since then, topological analyses have been widely adopted in attempting to uncover patterns in biology (Jeong, Tombor, Albert, Oltvai, & Barabasi, 2000), ecology (Montoya & Sole, 2002), linguistics (Cancho & Sole, 2001), and transportation (Carvalho & Penn, 2004; Jiang & Claramunt, 2004; Salingaros & West, 1999). Topology represents an effort to uncover structure and pattern in these often complex networks (Buhl et al., 2006). The topological approach to measuring street networks, for instance, is primarily based upon the idea that some streets are more important because they are more accessible, or in the topological vernacular, more central (Porta, Crucitti, & Latora, 2006). Related to connectivity, centrality is another important topological consideration. A typical Union Station, for example, is a highly central (and important) node because it acts as a hub for connecting several different rail lines. Some common topological measures of centrality include Degree and Betweenness, which we will discuss in more detail in subsequent sub-sections.
There are also some peculiarities worth remembering when it comes to topology.
When thinking about the Size of a network, our first inclination might be measures that provide length or area. In topological terms, however, Size refers to the number of nodes in network. Other relevant size-related measures include: Geodesic Distance (the fewest number of links between two nodes); Diameter (the highest geodesic distance in a network); and Characteristic Path Length (the average geodesic distance of a network).
Density is another tricky term in the topological vernacular. When talking about the density of a city, we usually seek out measures such as population density, intersection density, or land use intensity. In most cases, these metrics are calculated in terms of area (e.g. per km2). In topology, however, Density refers to the density of connections. In other words, the density of a network can be calculated by dividing the number of links by the number of possible links. Topologically, the fully-gridded street networks of Portland, OR and Salt Lake City, UT are essentially the same in terms of Density; with respect transportation and urbanism, however, there remain drastic functionality differences between the 200’ (~60m) Portland blocks and the 660’ (~200 m) Salt Lake City blocks.
As illustrated with the Portland/Salt Lake City example, one limitation of topology is that it ignores scale. However, this can also be an advantage. For instance, Denver might be much closer to Springfield, IL than Washington, DC as the crow flies, but a combination of several inexpensive options for direct flights to DC and relatively few direct flight options for Springfield mean that DC is essentially closer in terms of network connectivity. Topology captures such distinctions by focusing on connectedness rather than length.
While topological analyses such as the above are scale-free, we also need to be careful about use of this term because scale-free networks are not equivalent to scale-free analyses. In topological thinking, scale-free networks are highly centralized. More specifically, if we plot the number of connections for each node, the resulting distribution for what is known in topology as a scale-free network would resemble a Power law distribution with some nodes having many connections but most having very few. A hub-and-spoke light-rail system, for instance, tends to exhibit scale-free network qualities with relatively few stations connecting many lines. The nodes in a random network, on the other hand, tend to have approximately the same number of connections. For instance when we define the intersections of a street network as the nodes and the segments as the links, the results tends towards a random network. If we flip the definition again, so that the streets are the nodes and the intersections the links, we trend back towards a scale-free network (Jiang, 2007; Jiang & Claramunt, 2004).
One reason to look at connectivity in these terms has to do with the critical issues of resilience and vulnerability. In general, robustness is associated with connectivity. When we have good connectivity, removing one node or link does not make much of a difference in terms of overall network performance. In contrast, scale-free networks are more susceptible to strategic attacks, failures, or catastrophes. However, as shown in a recent paper about urban street network topology during a Zombie apocalypse, good connectivity could actually be a double-edged sword (Ball, Rao, Haussman, & Robinson, 2013).
Most people I talk with about roundabouts probably fall somewhere on the love-hate spectrum between extreme dislike and hate. One reason for such an unenthusiastic assessment – especially for Americans – can likely be traced to some common misconceptions about what we are actually talking about when we talk about roundabouts.
Having grown up near Boston, I spent countless Friday evenings on hot summer nights sitting in traffic with everyone else trying to get to Cape Cod. The Cape Cod Canal cuts the area off from the rest of Massachusetts, so the only way across by car meant traversing one of the two bridges. For years, both bridges also had what those from New England called a “rotary” on one side or the other. Traffic would routinely back up for miles at both bridges, making what could have been a 75-minute drive considerably longer. The culprit was often these multilane rotaries. Such intersections proved to be inefficient – both in terms of traffic flow and land consumption – as well as dangerous. You may also remember Clark Griswold getting stuck in a London traffic circle in the 1985 movie European Vacation. “Hey look kids, there’s Big Ben! Parliament!” Homer Simpson was in a similar situation in a 2003 episode and nearly killed the Queen of England! Examples like these end up giving all circular intersections a bad rap. Not surprisingly, circular intersections were essentially phased out of most U.S. design toolboxes and the minds of many Americans.
Modern roundabouts, not developed until the 1960s, refer to something quite different. For one thing, roundabouts are much smaller than the old rotary intersections. Instead of outside diameters exceeding 300’ or 400’, modern single-lane roundabouts typically range between 90’ and 180’. Another thing is that the cars entering must yield to the cars already in the roundabout (although this was usually, but not always, the case with the older traffic circles and rotaries). The main defining characteristic of modern roundabouts, however, has to do with speed deflection. Speed deflection refers to angle at which cars enter the roundabout. With the old rotaries, there was little to no horizontal deflection of through traffic so cars could easily exceed 30 mph. A well-designed modern roundabout typically has enough deflection in the angle of this approach to actively manage vehicle speeds to less than 20 mph. It can also still handle truck traffic with design features such as a traversable apron that skirts the inner circle, which can be seen in the top image from Vancouver.
So what does the research tell us about modern roundabouts? In most contexts, they move traffic more efficiently and are safer than conventional intersections. Why would this be the case? In terms of efficiency, there is no waiting for the light to turn green when there is no cross traffic. In fact, single-lane roundabouts have been shown to reduce delays as compared to conventional intersections and effectively manage traffic flows as high as 25,000 cars per day. Less idling also means fewer emissions. In terms of safety, roundabouts eliminate conflict points and the most dangerous types of conventional intersection crashes; while you may get more sideswipe or rear-end crashes, such crashes are far less likely to be fatal or severe injury. Also if the roundabout is designed with adequate deflection, these crashes tend to happen at slower speeds. This reduces crash severity to the tune of 78-82% fewer serious injury or fatality crashes as compared to conventional intersections (AASHTO Highway Safety Manual).
There are valid concerns about pedestrians and bicyclists in roundabouts, but splitter islands, setback crosswalks, and sidewalks – when combined with slower vehicle speeds – help tremendously. Interestingly, many places allow bicyclists to act as either a vehicle or a pedestrian in roundabouts. My own concerns center more on effectively serving those with impaired vision, which is still an issue with most roundabouts.
While I would argue that multilane roundabouts are unnecessarily used in many situations where a one-lane roundabout would work well, multilane roundabouts still offer many of the same advantages. Compared to single-lane roundabouts, however, they do: lose some speed deflection when volumes are low; introduce a new crash type to the mix (i.e. sideswipe crashes due to lane changes); and make things more difficult for pedestrians and bicyclists. You can also include neighborhood traffic circles – which are even smaller than most modern roundabouts – in this overall discussion of circular intersections. The example below from Berkeley, CA combines 4-way stop control with a circular intersection. While not quite a roundabout, it is a good example of using a small traffic circle to help manage speeds and improve safety.
Compared to signalized intersections, roundabouts are generally less expensive, more efficient, more environmentally friendly, and perhaps most importantly, safer. Furthermore, you never have to worry about a power outage with a roundabout. While there are legitimate reasons not to use roundabouts in some situations – such as highly unbalanced traffic flows or ROW limitations – many get eliminated as an option due to our cultural biases against them. All we are saying is give roundabouts a chance.
Why do traffic engineers seem to like one-way streets so much? The AASHTO Green Book points out a handful of efficiency advantages . By removing the delay caused by left-turning cars, we increase traffic capacity and speed. Fewer intersection conflicts means more efficient signal timings and, in theory, fewer and less severe crashes (e.g. by eliminating head-on crashes). Medians are no longer necessary, so you can often fit in an extra lane of through traffic, which further increases capacity and speed. More mobility with better safety? What’s not to love?
Beyond the abundant advantages, AASHTO lists a few disadvantages as well. There is the potential for increased travel distances in cases when you have to travel almost around a whole block to reach your destination. When all lanes begin to back up at traffic lights, emergency vehicles may be blocked. Lastly, one-way streets may confuse visitors.
Given AASHTO’s list of pluses and relatively few minuses, it makes sense why so many of our streets send traffic in just one-direction. Then again, it’s not hard to argue that what AASHTO deems an “advantage” might be the opposite. If I lived or worked on a one-way street, I’d be pretty hard-pressed to believe that more cars moving at higher speeds is necessarily a good thing.
Appleyard’s early studies found many residential livability advantages on the two-way streets, but the one-way street he investigated had far more traffic than the two-way comparisons . Denver converted a handful of one-way pairs to two-way operation in the early 1990s and found that residents preferred the change . A recent case study out of Louisville looked at a handful of one-way to two-way conversions and found significant increases in pedestrian traffic, property values, and business revenue . These benefits were accompanied by a significant decrease in crime. Other cities such as Charleston, SC and Lubbock, TX also found success in terms of two-way streets helping downtown revitalization [5, 6].
Such livability benefits are all well and good, but are they worth the increased road safety risks that AASHTO made clear? The research is beginning to suggest that the safety answer isn’t clear cut. Lubbock found no significant change in terms of traffic volumes or safety . Another study from Jerusalem also found no difference in road safety . Despite similar traffic levels on the Louisville conversions, crash rates dropped with the two-way streets . Moreover, child pedestrian injury rates on one-way streets have been found to be more than double the rates on two-way streets .
More research is needed on the safety outcomes. However, it is also interesting to ask why the safety benefits of one-way streets be overblown. First, there are likely to be differences in driver behavior, most notably with faster speeds on one-ways. It is pretty easy to understand see why slower traffic – despite the noted increase in conflict points – might help reduce crash severity. Another ITE guide even says the following regarding the safety of one-way streets: “one-way pairs with good signal progression and high travel speeds seemed to elicit red-light running behavior” .
The image below comes from the ITE Traffic Engineering Handbook. It makes the case for better safety on one-ways by depicting the number of conflict point at an intersection for a two-way street as 32 and for a one-way street as only 5. This is a stark difference that could theoretically result in better safety. Beyond the fact that conflict points are not often well correlated with actual safety outcomes , the bigger issue is that they are comparing apples and oranges. This diagram compares an intersection where all four-legs have two-lanes to an intersection where all four-legs have one-lane. In reality, the one-way streets would have at least two lanes, if not three as in the image from Denver above or in cases where the median is removed. One-way streets with multiple lanes is a fairer comparison that would substantially increase the number of potential conflict points and deem the comparison in the image below as meaningless.
Moreover with regard to conflicts, AASHTO even suggests converting from two-way to one-way operation in situations where an urban street has too many pedestrian-vehicle conflicts . The reduction in pedestrian-vehicle conflicts is supposedly derived from a simpler set of intersection movements. The real reason for the reduction of pedestrian-vehicle conflicts might be even simpler: fewer pedestrians wanting to cross the street in the first place.
So after all that, the only definitive advantage left for one-way streets is increased traffic capacity. However, this point is also up for debate. Taking into account the decreased accessibility of circuitous routes, one paper found that drivers make significantly more turning movements and travel greater distances given the same origins and destinations in a network dominated by one-way traffic patterns . Another more recent paper suggests that a network of two-way streets actually has a greater trip-serving capacity – particularly for trips less than 5 miles – as compared to a network of one-way streets . When also prohibiting left-turns in the two-way network, this capacity advantage of the two-way network included longer trips as well.
ITE COMPARISON OF INTERSECTION CONFLICT POINTS 
Not only do one-way streets hinder accessibility and livability, but the traffic engineering benefits don’t necessarily seem to hold. While one-way streets are still needed when relatively narrow cross-sections prevent two-way traffic, in most other urban contexts, it is hard to imagine why so many cities continue to preserve one-way streets. Some cities are changing their ways. The before-and-after images at the top are from Larimer Street in Denver where a mile-long stretch was recently converted from a one-way into a two-way. Instead of three high-speed lanes heading toward downtown, there is now one lane in each direction with accompanying bike lanes. With noticeably slower traffic and more active transportation use along this corridor, it makes sense why there so many new businesses seem to be popping up, especially when compared to the parallel streets that remain focused on one-way traffic. It might be time for cities to find a new direction – and more research is needed – but it seems like this this new direction will run both ways.
AASHTO, A Policy on Geometric Design of Highways and Streets. 2011, Washington D.C.: American Association of State Highway and Transportation Officials.
Appleyard, D. and M. Lintell, The Environmental Quality of City Streets: The Residents’ Viewpoint. Journal of the American Institute of Planners, 1972. 38(2): p. 84-101.
Robert Dorroh, I. and R. Kochevar, One-Way Conversions for Calming Denver’s Streets, in ITE International Conference. 1996, Institute of Transportation Engineers: Las Vegas, NV.
Riggs, W. and J. Gilderbloom, Two-Way Street Conversion: Evidence of Increased Livability in Louisville. Journal of Planning Education & Research, forthcoming.
Baco, M.E., One-Way to Two-Way Street Conversions as a Preservation and Downtown Revitalization Tool: The Case Study of Upper King Street, Charleston, South Carolina, in Historic Preservation. 2009, Clemson University and College of Charleston: Clemson, SC.
Hart, J., Converting Back to Two-Way Streets in Downtown Lubbock. ITE Journal, 1998. August.
Hocherman, I., A.S. Hakkert, and J. Bar-Ziv, Safety of One-Way Urban Street. Transportation Research Record, 1990. 1270: p. 22-27.
Wazana, A., et al., Are Child Pedestrians at Increased Risk of Injury on One-Way Compared to Two-Way Streets? Canadian Journal of Public Health, 2000. 91(3): p. 201-206.
ITE, Toolbox on Intersection Safety and Design. 2004, Washington, D.C.: Institute of Transportation Engineers.
Jacobs, A.B., E. MacDonald, and Y. Rofe, The Boulevard Book: History, Evolution, Design of Multiway Boulevards. 2003, Cambridge, MA: The MIT Press.
Walker, G.W., W.M. Kulash, and B.T. McHugh, Downtown Streets: Are We Strangling Ourselves on One-Way Networks? Transportation Research Circular, 2000(501).
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SPONTANEOUS ROAD USER PRIORIZATION IN SHARED SPACE INTERSECTIONS (red line = 1:1 ratio of pedestrians to vehicles; hollow circles = pedestrian-dominated intersections; blue circles = vehicle-dominated intersections; circle size = higher level of modal dominance when conflict arose) by Wesley Marshall and Nick Ferenchak
Like we said last time, shared spaces are streets where all signs, traffic control devices, street markings, and separation of modes have been removed. This way of thinking forces all road users, no matter the mode of transportation, to take responsibility for their own actions and negotiate the space via all the other road users by means of eye contact and other social cues. This is in stark contrast to a conventional street design where modes tend to be separated and movements guided and controlled by traffic signals and the like. In the right context, the result of shared space is not chaos; instead, spontaneous order takes hold, resulting in a space often more efficient and safer than a conventional design.
Shared space is an often misunderstood concept. First things first; the right context is key. Shared spaces would not work everywhere, especially when the focus is mobility and high travel speeds. The surrounding land uses and the way that these buildings and activities interact with the street make a big difference. So does the mix of road users. A street dominated by cars would be hard pressed to function like we might imagine a shared space should.
Many people believe living streets and/or woonerfs to be synonymous with shared spaces. However, these street types specifically grant priority in the street space to pedestrians. A true shared space concept does not. Why? Because it doesn’t have to. In the right context, this prioritization occurs naturally. The above graph is from a recent paper I wrote with my doctoral student Nick Ferenchak. We analyzed data from 37 shared space intersections with high levels of interaction between pedestrians and vehicles and assessed which mode acquiesced to which when a conflict arose. When vehicles outnumbered pedestrians, while controlling for other design factors, the pedestrians tended to back off and cede the road space to the cars. However when pedestrians outnumbered cars, this prioritization spontaneously flipped. Now, the cars were the ones yielding to the pedestrians when a conflict arose. The red line in the graph above represents the 1:1 ratio of pedestrians to vehicles. What we call the modal dominance index is represented by the size and color of the circles. The hollow circles signify pedestrian-dominated intersections while the blue circles represent vehicle-dominated intersections. The size of the circle indicate a higher level of dominance over the shared space.
Many shared space designers are tempted to follow the living street or woonerf model and grant pedestrians priority in the street space, to the point where there is a call for what is known as a Pedestrian Priority Shared Space (PPSS). While such designs can be successful and find a multitude of benefits, putting up signs to grant pedestrians priority misses a key point of the shared space concept. A true shared space in the right context doesn’t need those signs.