Study on Foot Traffic Flows on Pedestrian Routes In Underground Traffic System 1 Moscow State University of Civil Engineering 2 Academy of State Fire Service of Russia, UNK PPBS, EMERCOM 3 Ulyanovsk State Technical University Academy of State fire service of Russia Prof Valery Kholshevnikov 1, Dr Dmitry Samoshin 2, Dr Irina Isaevich 3 Moscow State university of civil engineering Ulyanovsk State Technical University

Study outline: Moscow underground traffic system: - 9 millions of passengers daily: - normal operation gives max load (compare to emergency evacuation): simultaneous multidirectional pedestrian movement: contra flows, flows crossings; - issues under discussion: particular technique of actual observations; relations between travel speed and density of human flows; metro car traffic capacity and station platform design; a mutual impact of escalator installations and pedestrian flows on efficiency of daily operation; an impact of automatic turnstile on evacuation route traffic capacity;

Traffic routes in underground station Blue arrows – towards trains Green arrow – towards exits Entrance Ticket hall Platform Trains Bridge to changing station Station hall Escalators Ticket control Trains

Data sample volume 5957 counts total: 3380 – travel speed measurements at different flow density range; 1379 – escalator traffic capacity depending upon flow density and flow speed; 396 – ticket machines traffic capacity; 301 – “widening” flows; 261 – flow density on platform; 244 – car door traffic capacity;

Methods of actual observations – video analysis Scale grid drawing Videotape analysis based on scale grid. An example.

Travel speed (without density impact). Empirical data. Non Rush-hour Rush-hour Average free travel speed 69.4 m/min Average free travel speed m/min

Travel speed and emotional state Relationship between emotional state and activity: 1 – attention; 2 – control; 3 – activity. Relation between unimpeded travel speed and psychological stress level Quiet Active Of increased activity

Rush-hour – “Of increased activity” category Non Rush-hour – “Active” category

General law for V=f(D) -is the average travel speed of pedestrians in a flow, m/s; -is the average travel speed of pedestrians on a route without the influence of density, m/s; a j -is an empirical constant for each type of pathway); Di -is the prevailing density of the flow, persons/m 2 (or m 2 /m 2 ); Doj -is a threshold value of flow density on the j-the pathway, persons/m 2 (or m 2 /m 2 if pedestrians are measured based on their horizontal projection) ); E -is an indicator of the emotional state of the pedestrian (the category of movement); J -is an indicator of the type of route traversed; D, person/m 2 V, m/min Horizontal plane Door opening D, person/m 2 V, m/min Stairs downward Stairs upwards D, person/m 2 V, m/min D, person/m 2 V, m/min

Parameters of pedestrian flow used in Russian building codes for emergency evacuation

Relation between travel speed, emotion level and density of flow in underground traffic system. Of increased activity Active V=106.2*(1-0.4*Ln(D/0.56)) V=69.4*(1-0.4*Ln(D/0.65)) D, persons/m 2 V, m/min

Pedestrians on platform Camera Pedestrians Camera marks Car door traffic capacity – 50 persons/min (at door width 1.2m); Max platform density – 5 persons/m 2 ; Comfortable inter-person distance: face to face – 0.49m, face to back – 0.58m, side by side – 0.8m.

1 – station hall; 2 – movement through escalator’s guiding handrails; 3 – handrails in front of escalator; 4 – escalator entrance; 5 – escalator. Movement through escalator

Pedestrian flow in front of escalator Escalator Station hall Trains: 500 pers t=0.15 min t=2.02 min t=4.20 min

Escalator traffic capacity Maximum escalator traffic capacity obtained at close values between pedestrian speed m/min (at density 5 persons/m 2 ) and escalator speed 42 m/min (0.7 m/sec)

Movement through ticket machines Ticket machines Area of observation Passengers In rush-hour and normal operation time to overcome ticket machines, their traffic capacity and flow density impact were investigated

Time losses moving through ticket machines Flow density and psychological state impact passage time: the higher the density the more time takes to pass through ticket machine due to physical contacts between people and stress factor. In normal condition at 2-3 persons/m 2 time decreases because passengers aimed to overcome uncomfortable type of route. Average traffic capacity is 1187 persons per hour.

Pedestrian flow modeling Based of study results, valid computer programs were developed. On this diagram, comparison of actual observation and flow modeling at control point is presented.

Distinguishing features of pedestrian movement in underground traffic system 1. Seasons (i.e. winter, summer etc) do not influence pedestrian movement. 2. Psychological state of pedestrians (i.e. rush-hour, normal conditions) change parameters of their movement. 3. Rush–hour movement fit “of increased activity movement” category of movement, and non-rush hour movement fit “active” category of movement. 4. Pedestrians in rear of the flow moves in “quiet” category of movement in rush hour and in normal conditions. Pedestrians in head of flow moves in “of increased activity” category of movement in rush hour and in normal conditions 5. It was noticed “widening” of the flow as they exit on a wide hall. Flow widening caused with pedestrian intention to move in a low density extending length of their route. Balance between uncomfortable movement in a dense flow and roundabout route observed at density value about 1,2 pers/m 2 (range 0,3-1,9 pers/m 2 ) – flow does not widening any more.

General conclusions Observations were undertaken on all consecutive route sectors based on unified technique and analytical methods aimed to get the most precise data. Experimental data were fully statistically treated in each density range. Unimpeded travel speed (i.e. without density impact), as an indictor of emotional state, confirmed established earlier scale of emotional states (categories of movement) and relation between parameters of pedestrian flow based on Weber-Fechner law. Reliable data, describing human flows development and movement were obtained during these experiments. Validated against available data computer models were also developed and they used in practice nowadays.

Computer model ADLPV (Analysis of Pedestrian Flow, Probability) Changes in consequent time intervals Basic equations Density of flow: Number of pedestrians, passing to next sector of route: Transition travel speed: Transition time: