Transportation Geography and Network Science/Wayfinding

WayfindingEdit

ConceptsEdit

Travel behavior is a feedback process focusing on "optimizing" the movements of a traveler by spatial knowledge acquisition. This gathering process is composed by: navigation and wayfinding (or pathfinding). The first refers to the set of plans and actions required for unobstructed travel by locating position of objects, and plotting possible trajectories of travel. The second refers to the process of selecting a trajectory (i.e. path) from a choice set (also generated by the traveler following specific rules) connecting origin-destination pairs of interest.[1]

Wayfinding is implicitly linked to travel; spatial information accumulates while traveling. Newly added knowledge helps the human traveler find distinct possible trajectories to destinations of interest more efficiently. Therefore, human travelers do not only look to gather knowledge, but they also process it (learning mechanism) to perform more "optimal" travel decisions. Generally, this knowledge is accumulated not only on the form of angles of turns, number of turns, length of segments, but also critical environmental points, and other features in the vicinity of the paths. In other words, an individual may designate points perceived as important (work, home, and others) as anchor points or landmarks, in order to discern the locations of distinct places, and to facilitate the navigation among them. Furthermore, it should be noted that other sources such as maps, conversational exchanges, and others (besides actual travel) may also add to the knowledge of the environment of travelers in different forms (and effects).[2][3]

Spatial learningEdit

It has been identified that travelers behave significantly different according to their familiarity of the spatial environment (e.g. geometrical configuration), and the environmental complexity (e.g. too many details to remember).[4]

Familiarity EffectsEdit

Familiarity is related to navigational frequency, and thus in turn places with high familiarity positively correlate with an individual's level of spatial knowledge. Moreover, familiarity is divided in two components: local experience, and global experience. The former refers to detailed spatial knowledge of places of the network. The latter refers to notable elements of the environment as a whole (e.g. hierarchy of roads, famous landmarks, and others).

The level of familiarity directly affects the decision-making process of an individual; decisions are based on current spatial knowledge. For example, a daily commuter is able to retrieve past information (mostly local experience) that is complemented with newly acquired knowledge during travel (i.e. traffic condition changes). In the case of a visitor in a strange city, most of the information is based on global experience (perhaps acquired via maps), and as the travel proceeds is complemented with local experience. In general, the daily commuter is able to recognize (and expect) certain travel events (e.g. location of traffic signals) with higher accuracy than a visitor in a strange city. In addition, familiarity differs among individuals in other forms (i.e. distinct anchor points, dissimilar local experience, and others) besides level.[5]

Environmental ComplexityEdit

The geometrical configuration of the spatial environment plays a significant role in an individual's learning mechanism. Evidence suggest that highly differentiated areas (e.g. changes in housing styles, lot density, increases in neighborhood vegetation, changes from rectilinear to curvilinear patterns, and others) hasten the learning process of individuals. In contrast, homogeneity along paths hinders the storing and retrieval process of spatial information for decision-making; it is harder to identify which area is related to the origin-destination pair of interest.

Other evidence suggest that longer elapse periods of travel between an origin-destination pair of interest hinders the storing of information. The traveler will require to remember too many details. This difficulty increases when the traveler is also unfamiliar with the environment. This is not to say that high familiarity nullifies the effects of complexity. Travelers in highly familiar settings will still overlook spatial details, and regard them as nonessential in order to cope.[6]

Wayfinding in Transportation NetworksEdit

In transportation research, path finding and choice is a fundamental process of interest. In essence, a traveler explores the network and ultimately chooses a path connecting any two origin-destination pairs from several feasible alternatives. This behavior is influenced by characteristics from both the traveler and the physical infrastructure. The traveler’s attributes consist of objective elements (age, gender, income,...) and subjective elements (preferences, perception, experiences...). In contrast, the physical infrastructure is composed of the transportation network. Additionally, this selection is a feedback process, where traveler’s previous decisions influence future decisions.[7]

Typically, path finding and choice behavior (a subset of travel behavior) has focused on three areas: traveler’s knowledge of alternative routes, route decision processes, and route choice preferences due to attributes of the traveler-road network system. The first consists of analyzing the criteria (shortest path, fastest path...) travelers adopt to generate their set of possible routes, the second focuses in the rules (preplanning, Markov process, and intermediate process) for the execution of the decision, and the last examines the effect of attributes in the route choice preference.[8]

Choice Set GenerationEdit

Travelers consider only a subset of the set of feasible paths between origin-destination pairs of interest, because of a myriad of reasons including: limited information (i.e. traveler is unaware of other alternatives), personal preferences (e.g. long distance travelers restrict their choices to highways), suboptimal solutions to objective criteria such as minimizing travel time (other travelers may consider minimizing distance or other objectives), and others. This subset becomes the travelers path choice set, and thus an optimal choice will be selected considering only this subset. [9]

There are different algorithms for choice set generation in the transportation research literature. The most common includes the labeling method. It consists of using the attributes of links such as travel time, travel distance and functional class to include in generalized cost functions for each alternative routes. Moreover, these route are labeled according to the attribute of interest and the objective criteria (e.g. minimize time, minimize distance, and so on). In this way, feasible choice sets may be generated for each origin-destination pair of interest for the travelers. Other algorithms may be simulation-based (i.e. sampling from probability distributions), or more heuristics (e.g. link penalty method).[10]

A fairly general treatment may be found in Ramming's PhD disseration[11]

Decision ProcessEdit

Travelers must have a criteria and a time point for selecting their routes from their choice sets. For example, travelers may preplan their choice before starting a trip. Travelers may evaluate their choices at each junction (i.e. choose a new option at each node of a network). Travelers also may preplan and reevaluate at each junction. In addition, travelers must also have a set of decision rules allowing them to choose their option from the choice set. These decision rules may be based on distinct hypothesized abstractions of behavior including: utility maximization (travelers have utility functions representing their preferences, and thus choose the option with the highest utility); satisficing (travelers choose the first alternative that surpasses their threshold levels); elimination by aspects (alternatives with values lower than threshold are ignored); and lexicographic rules (travelers choose the route with the highest value for the most important attribute, if there is a tie the traveler chooses the route with the highest value for the second most important attribute and so on). [12]

Utility maximization is the most common approach. It is a mathematical formulation encapsulates three assumptions: transitivity (if a consumer prefers a over b, and it prefers b over c, then it must prefer a over c), completeness (consumers have preferences between all bundles of goods), and reflexiveness (preferences are related to themselves; indifference is accounted for). In addition, there are two approaches for utility maximization: deterministic (consumers are well defined and have access to all the relevant information), and probabilistic (typically is assumed that the ordering of the preferences is random, because researchers may ignore certain attributes in the utility functions; see random utility models). [13]

Others recent criteria may consider search processes, spatial information, learning mechanism, and others. A detailed treatment may be found in Zhang's PhD Dissertation. [14]

Attributes of Choices and Decision-MakersEdit

Most of the research indicates that travel time and travel distance are the main explanatory factors behind path choices. [15][16][17][18][19] However, other factors (such as aesthetic scenery, network knowledge, and trip information) are also linked to the explanation of this path finding and choice. In addition, socio-demographic also being show to influence the path finding and choice behavior of travelers. The most important attributes according to empirical research are listed below.[20]

Traveler AttributesEdit

  1. Socio-demographic (age, gender...)
  2. Value of Time (travelers may weigh time periods differently)
  3. Past Experience (new decisions are based on previous decisions)
  4. Trip Purpose (work commute, leisure traveling...)
  5. Famialirity (knowledge of the network as mentioned previously)
  6. Information (current knowledge of traffic conditions or other)

Path AttributesEdit

  1. Travel time
  2. Travel distance
  3. Travel cost (e.g. tolls)
  4. Time of day
  5. Roadway characteristics (i.e. geometrical configuration and infrastructure conditions)

ExperimentsEdit

Transportation researchers have developed several experimental designs to study the behavior of travelers under multiple choice decisions. In this way, estimates of travelers preferences with regards to distinct attributes of the choice situations (paths related to wayfinding), and related to their own personal characteristics (typically travelers are grouped through market segmentation methods) may be obtained.

Generally, the experimental designs vary by degree of realism, and by the source of the data collected (hypothetical choices vs. observed choices). In addition, two groups of experimental designs exist in path finding and choice studies: (quasi) laboratory experiments, and field observations. The former includes: paper-based experiments (e.g. multiple choice questions), experiments with visual aids (e.g. questions with charts, maps), and simulations (e.g. computer-based simulations, and fixed-base vehicle simulators). The latter includes interviews in person or through the phone; self-completion questionnaires; stalking/shadowing the subjects (e.g. license plate matching), and recently GPS-tracking of the subjects.[21]

Data Collection TechniquesEdit

Several techniques used for route/path finding and choice studies are presented in the following list based on Carrion and Levinson's paper[22]:

  1. Questionnaires with Hypothetical Scenarios.
  2. Questionnaires with Hypothetical Scenarios including visual aids.
  3. Computer-Based Simulator
  4. Fixed-base Vehicle Simulators
  5. Field Self-Completion Questionnaires.
  6. Field Interviews.
  7. Field GPS Tracking

ApplicationsEdit

Wayfinding and Traffic Assignment do not appear at first glance to be overlapping areas in transportation research. However, it should be pointed out that traffic assignment models builds upon behavioral assumptions of the travelers (do they have perfect information? do they minimize travel time? and so on). Thus, the models from the most basic one (all or nothing assignment) to the most complex ones (dynamic traffic assignment) have fundamental assumptions with regards to the behavior of travelers wandering around in the network, and optimizing their choices.

Traffic Assignment and Behavioral AssumptionsEdit

Traffic assignment may be defined as an iterative process where flows (travel demand) are allocated in a network according to a criteria, and under specific assumptions. There are several types of traffic assignment (actually the classification may vary): congestion-invariant assignments; traffic assignments with congestion; stochastic traffic assignment; stochastic traffic assignment with stochastic travel times; day to day traffic assignment; and dynamic traffic assignment. All of these except for the last two belong to the static mathematical program theory (linear programs, nonlinear programs, and stochastic programs), and the last two are dynamic mathematical programs.

The basic traffic assignment model is by Wadrop[23]. It is defined by two equilibrium: User and System. The User Equilibrium (UE) refers to the assignment of flows to a network subject to each user minimizing their own travel time (behavioral assumption which implicitly includes perfect information and rationality), and the UE is obtained when users cannot find better alternative paths. In the System Optimal (SO), the assignment is of flows is subject to minimizing the whole network overall travel time. The results of these equilibrium also varies by the assumption of the link travel time functions (link-flow or link impedance). The BPR link travel time function is fairly popular to simulate congestion during the traffic assignment process (see the link below). Other forms based on the original basic Wardropian model change the behavioral assumptions by indicating perception of travel times instead of actual travel times (stochastic user equilibrium, where a perception error is added as a random variable), and also risk behaviors may be included in the model by considering risk functions such as exponential forms (typically used for traffic assignment considering travel time reliability)[24].

In more complicated models such as day-to-day traffic assignment where the propagation dynamics matter, rules are taken with regards to how travelers decide to switch from a path to another at each time event. These rules may be based on linear travel time differences at a time event t, or even decreasing weights comparing travel time during all previous time events. In other words, the travel time experienced yesterday is more important for future decision in contrast to the travel time experienced the day before yesterday and so on.


Other sources for Traffic AssignmentEdit

Fundamentals of Transportation/Traffic Assignment

Wayfinding DesignEdit

Wayfinding design refers to the planning of facilities with spatial requirements considering explicitly the wayfinding process of individuals. Wayfinding "efficient" designs include the use of signs, architectural forms, and other information sources that are readily available to the users, and help the users in their path finding and choice process quickly. Traditionally, facility design has always been a process performed by architects and urbanists. However, it also includes graphic designers for the production of legible and clear signs, and building managers and personnel for the maintenance and continuous improvement of the facilities.

Generally, the design must consider the location and the presentation of information to the users as a vital component. Examples can be seen in shopping malls which include maps of the area, and also points indicating the location of the maps with respect to the whole facility. Typically these maps are located close to the entrances of the facilities, and disseminated internally at a fixed spacing between them. Other important components include the location of the entrance/exit doors. These are vital as well because of safety concerns (evacuation procedures) and accessibility (near the geometrical centroids) to the facilities. In addition, other information sources as interactive signs, flashing lights, sirens, and others may be included to continuously inform the users of changes in schedule or conditions of the facilities.[25] Map orientation (North is Up) is not a global standard, though common in the US and Europe, is not found in Japan, e.g.

According to some research, maps are superior to landmarks for learning to navigate. [26]

ReferencesEdit

  1. Carrion, C. (2010), Value of reliability: Actual commute experience revealed preference approach, Master’s thesis, University of Minnesota, Twin Cities (USA).
  2. Golledge, R. (1992), “Place recognition and wayfinding: Making sense of space”, Geoforum , Vol. 23, pp. 199–214.
  3. Golledge, R. (1999), Human wayfinding and cognitive maps, in ‘Wayfinding Behavior: Cognitive Mapping and Other Spatial Processes’, John Hopkins, pp. 5–45.
  4. Golledge, R. and Stimson, R. (1997), Spatial behavior, Guilford Press, New York.
  5. Golledge, R. (1999), Human wayfinding and cognitive maps, in ‘Wayfinding Behavior: Cognitive Mapping and Other Spatial Processes’, John Hopkins, pp. 5–45.
  6. Golledge, R. (1992), “Place recognition and wayfinding: Making sense of space”, Geoforum , Vol. 23, pp. 199–214.
  7. Bovy, P. and Stern, E. (1990), Route Choice: Wayfinding in Transport Networks, Kluwer Academic Publishers, Netherlands.
  8. Ben-Akiva, M., Bergman, M., Daly, A. and Ramaswamy, R. (1984), Modelling interurban route choice behaviour, in ‘Proceedings of the Ninth International Symposium on Transportation and Traffic Theory, Delft, the Netherland’, pp. 299–330.
  9. Ben-Akiva, M., Bergman, M., Daly, A. and Ramaswamy, R. (1984), Modelling interurban route choice behaviour, in ‘Proceedings of the Ninth International Symposium on Transportation and Traffic Theory, Delft, the Netherland’, pp. 299–330.
  10. Bovy, P. and Stern, E. (1990), Route Choice: Wayfinding in Transport Networks, Kluwer Academic Publishers, Netherlands.
  11. Ramming, M. (2001), Network Knowledge and Route Choice, PhD thesis, Massachusetts Institute of Technology (USA).
  12. Ben-Akiva, M., Bergman, M., Daly, A. and Ramaswamy, R. (1984), Modelling interurban route choice behaviour, in ‘Proceedings of the Ninth International Symposium on Transportation and Traffic Theory, Delft, the Netherland’, pp. 299–330.
  13. McFadden, D. (1981), Econometric models of probabilistic choice, in ‘Structural analysis of discrete data with econometric applications’, MIT Press.
  14. Zhang, L. (2006), Search, Information, Learning and Knowledge in Travel Decision-Making: A Positive Approach for Travel Behavior and Demand Analysis, PhD thesis, University of Minnesota, Twin Cities (USA).
  15. Trueblood, D. L. (1952), “Effect of travel time and distance on freeway usage”, Highway Research Board , pp. 18–37.
  16. Michaels, R. D. (1966), “Attitudes of drivers toward alternative highways and their relation to route choice”, Highway Research Record , pp. 50–74.
  17. Kansky, K. J. (1967), “Travel patterns of urban residents”, Transportation Science , Vol. 1, pp. 261–285.
  18. Vaziri, M. and Lam., T. N. (1983), “Perceived factors affecting driver route decisions”, Journal of Transportation Engineering , Vol. 109, p. 297-311.
  19. Hamerslag, R. (1981), “Investigation into factors affecting the route choice in rijnstreek-west with the aid of a disaggregate logit model”, Transportation , Vol. 10, pp. 373–391.
  20. Pal, A. (2004), Modeling of commuter’s route choice behavior, Master’s thesis, The University of Toledo (USA).
  21. Bovy, P. and Stern, E. (1990), Route Choice: Wayfinding in Transport Networks, Kluwer Academic Publishers, Netherlands.
  22. Carrion, C. and Levinson, D. (2010), Value of reliability: High occupancy toll lanes, general purpose lanes, and arterials, in ‘Conference Proceedings of 4th International Symposium on Tranportation Network Reliability in Minneapolis, MN (USA)’.
  23. Wardrop, J. (1952), Some theoretical aspects of road traffic research, in ‘In Proceedings of the Institution of Civil Engineers’, Vol. 1, pp. 325–378.
  24. Chen, A., Ji, Z. and Recker, W. (2002), “Travel time reliability with risk-sensitive travelers”, Transportation Research Record: Journal of the Transportation Research Board , Vol. 1783, pp. 27–33.
  25. Arthur, P. and Passini, R. (1990), Wayfinding: People, Signs and Architecture. Toronto: McGraw-Hill Ryerson
  26. Fields, A.W., Shelton, A.L. (2006). Individual skill differences and large-scale environmental learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(3), 506-515.
Last modified on 1 May 2011, at 23:39