4. Comparison of pedestrian traffic in Germany and in China
4.3 Empirical studies on pedestrian and driver behaviour
4.3.3 Interactions and conflicts
Possible interactions and conflicts between pedestrians and different traffic streams at a standard four-arm intersection are listed in the interaction/conflict matrix (Figure 32). The traffic stream includes not only motorised vehicles, but also bicycles parallel released with motorised vehicles.
traffic stream pedestrian
1 2 3 4 5 6 7 8 9 10 11 12 1-1 × • •
1-2 × • ×
2-1 × • •
2-2 × × •
3-1 × • •
3-2 • × ×
4-1 × • •
4-2 × • ×
Note:
×: Interactions between GW and permissive turning traffic
•: Conflicts between RW/EW/LW and conflicting traffic Figure 32: Interaction/conflict matrix
Additional interactions/conflicts happen between pedestrians and bicycles in China due to their battle for space. For example, bicycles stop ahead of the stop line, occupying part of crossings or pedestrians wait at bicycle lanes. Concerning different types of bicycles, interactions/conflicts with normal bicycles can be recognised to be safe, because manoeuvres can be easily executed by both pedestrians and normal bicycles in time. However, interactions/conflicts between pedestrians and electric bicycles ought to be taken into account, because the electric bicycles are always heavier, have higher speed than normal bicycles, and their trajectories are more flexible than vehicles, which brings new danger to pedestrians.
In order to describe interactions/conflicts quantitatively, the following performance indices are introduced in this research.
Proportions of pedestrians with interactions
% 100
/ / /
int/conf /
/ / /
/ /
int/conf− = − ×
EW RW LW GW
EW RW LW GW EW
RW LW
GW N
p N (8)
where,
pint/conf-GW/LW/RW/EW: proportions of GW, LW, RW, EW with interactions (%)
NGW/LW/RW/EW-int/conf: number of GW, LW, RW, EW involved in interactions/conflicts (p) NGW/LW/RW/EW: number of GW, LW, RW, EW (p)
Levels of interactions and conflicts have been defined in Section 3.4.2. Interactions of level 0 (interactions obeying rules) and level 1 (safe interactions) happen between pedestrians crossing on Green and permissive turning vehicles, while conflicts of level 2 (light conflicts) and level 3 (serious conflicts) happen between vehicles and pedestrians who cross against signals (cf. Table 8).
Interactions/conflicts can be seen at each crossing observed in China, while interactions/conflicts only happen at two crossings among the eight crossings observed in Germany. It is also found that level 0, level 1 and level 2 are common forms in reality in both countries, level 3 happen at times in China, while near accidents (level 4) and accidents (level 5) are not observed.
(a) China (N=1874) (b) Germany (N=255)
Figure 33: Proportions of pedestrians involved in interactions/conflicts
Following results concerning proportions of pedestrians involved in interactions/conflicts are drawn out in Figure 33.
• GW are about 1.5 times more often involved in interactions in China (30%) than in Germany (22%). Vehicles yield to pedestrians more often in Germany, while in China is mostly the other way round.
• Totally 65% of the RW are involved in light conflicts (Level 2) in China, in which 58% of manoeuvres are taken by pedestrians who cross on Red (Level 2a); While in Germany, only 7% pedestrians crossing on Red are involved in light conflicts. It reflects that different attitudes towards non-compliance in two countries: most RW in China are prepared to be involved in conflicts during crossing, they prefer to cross lane by lane at multi-lane streets and accept smaller gaps, while in Germany most pedestrians crossing on Red only when they are sure that conflicting traffic is absent.
• More LW are involved in light conflicts in Germany.
• EW are seldom seen to be involved in conflicts in both countries.
Risk factor
Risk factor equals total number of interactions/conflicts divided by an “exposure”, which is determined by pedestrian volume and relevant vehicle volume (cf. Eq.9). It represents the probability of a pedestrian involved in interactions/conflicts per unit of exposure.
4.3 Empirical studies on pedestrian and driver behaviour 53 (9) where,
RGW/LW/RW/EW: risk factor of GW, LW, RW, EW (-)
nint/conf-GW/LW/RW/EW : total number of interactions/conflicts with GW, LW, RW, EW involved (p*veh)
NGW/LW/RW/EW : number of GW, LW, RW, EW (p)
Qveh: volume of relevant conflicting vehicles (veh)
Number of interactions is calculated by multiplying number of pedestrians and vehicles involved in an interaction at one time, for example, if one pedestrian yields to two vehicles at one time, the number of interactions is 1*2=2; if one vehicle yields to five pedestrians, the number of interactions is 1*5=5.
For GW, Qveh equals volume of permissive turning traffic; For RW and EW, Qveh equals volume of conflicting traffic during pedestrian Red; For LW, since the exposure to vehicles differs from pedestrian enter time, the later a pedestrian enters, the larger the exposure will be, therefore, Qveh
equals half of the volume of vehicles of next conflicting stage.
Moreover, considering different traffic conditions at the near side (with entrance lanes) and far side (with exit lanes), risk factor at near-side and far-side are analysed respectively.
Figure 34: Risk factor at near side and far side in China (N=1874)
Figure 35: Risk factor at near side and far side in Germany (N=225)
2 / 1 /
/ /
/ / / int/
/ /
/ ( GW LW RW EW veh)
EW RW LW GW conf EW
RW LW
GW N Q
R n
= − ×
Risk factor at investigated crossings in China and in Germany is analysed, as shown in Figure 34 and Figure 35. The value differences between China and Germany, between near side and fare side are listed as follows:
• Pedestrians crossing on Green have risks to interactions at near side in China while no risks at near side in Germany, since “Right turning on Red (RTOR)” is widely permitted in China but cautiously used in Germany.
• The majority of pedestrians crossing on Green yield to vehicles in China, while in most circumstances in Germany, vehicles yield to pedestrians.
• The probability of permissive turning vehicles yielding to pedestrians at far side is about two times higher than that at near side in China.
• Risk factor of RW in China is about 35 times higher than that in Germany.
• RW take high risks at near side in China while LW at far side, however, in Germany, RW have similar risks at near side and far side, and most conflicts involving LW happen at near side.
Model of total number of interactions/conflicts
In order to predict number of interactions/conflicts, a regression model is established based on the observation and it will be used in Chapter 5 for predict pedestrian behaviour of interactions/conflicts.
Previous studies have indicated close correlation between pedestrian accidents/conflicts and traffic volume (e.g. Zegeer, 1982; Zaidel, 1987). In this study, total numbers of interactions of GW (Level 0+Level 1) and conflicts of RW+EW (Level 2+Level 3) are regressed to have following correlation with volumes of pedestrians and relevant vehicle traffic during observation period (Eq.10). F test is passed and it proves a high goodness of fit.
071 . 4 ) ln(
652 . 0 ) ln(
996 . 0 int/
−
=
qped + qvehconf
e
N
(F=24.65>F(0.05)=0.00017) (10)where,
Nint/conf: total number of interactions/conflicts during observation period qped: number of pedestrians during observation period(p)
qveh: number of conflicting vehicles related to types of pedestrians (veh), for GW, it equals volume of permissive turning vehicles; for RW and EW, it equals volume of vehicles which are released during pedestrian red time.
Average interaction time of GW in China
Pedestrians behave variously when yielding to vehicles, they may stop, slow down, speed up, withdraw or change routes etc, which brings additional time delay for pedestrians. The average interaction time of GW can be calculated according to Eq.11. The calculation results of average interaction time of GW is 1.6 s at observed crossings in China.
1
1 1
, 1
) (
1
level
level GW n GW N
i level i level
n
N t
t t
evel GW
−
− ×
−
=
∑
−
(11)
where,
1 level
t : average interaction time of GW due to yielding to permissive turning vehicles(s/interaction)
4.3 Empirical studies on pedestrian and driver behaviour 55
1 level
NGW− : number of GW yielding to permissive turning vehicles (p)
1 ,level
ti
: crossing time of the ith GW yielding to permissive turning vehicles (s)
1 level
n : total number of interactions of level 1
Average delay of GW
Figure 36: Diagram of pedestrian waiting time
Total delay of GW in a cycle includes three parts, which are waiting time for Green (W1), pedestrian discharging time (W2) equals the sum of the start-up loss time and release time of the platoon (cf.
5.3.1.1), and interaction time due to interactions with permissive turning vehicles(W3). In Figure 36, the shadow area shows total delay of GW.
GW
GW N
W W
d =W1+ 2 + 3 (12)
2
) (
1
r N
W = NR − RW+EW × (13)
2 1.71 0.73 NR NwRW EW
W = + × − + (14)
W
3= n
level1× t
level1 (15)where,
dGW: total delay of GW (s) W1: waiting time for Green (s) W2: pedestrian discharging time (s) W3: total interaction time (s)
NGW: average number of GW in a cycle (p/cyc)
NRW+EW: average number of RW and EW in a cycle (p/cyc)
NR: average number of pedestrians arriving during red time in a cycle (p/cyc) w: width of crossing (m)
Proportion of pedestrians involved in very risky situations (PET< 4 s)
LW are more easily involved in very risky situations (PET< 4 s), especially at the far side since the oncoming vehicles are not expected to arrive so soon. For example, 77% of LW are involved at F(3,3,1) in the very risky situation. Besides LW, RW also have to face the risky situation in China, for example, 17% of RW have the average post encroachment time (PET) of 2.60 s at S(2,2,0)-1.