3. 論文導讀
Wei-Kuang Lai, Ting-Huan Kuo, Chi-Hua Chen, “Vehicle Speed
Estimation and Forecasting Methods Based on Cellular Floating
Vehicle Data,” Applied Sciences, vol. 6, no. 2, Article ID 47,
February 2016. (SCI, ISSN 2076-3417)
◦ DOI: 10.3390/app6020047
◦ Times Cited in Google Scholar: 3
◦ Impact Factor: 1.679
3
4. 前言
常見的交通資訊收集方法
◦ 車輛偵測器(Vehicle Detector, VD)
◦ 電子標籤偵測器(eTag Detector, ETD)
◦ 探偵車(Probe Car, PC)
◦ 蜂巢流動車輛資料(Cellular Floating
Vehicle Data, CFVD)
蜂巢流動車輛資料優勢
◦ 平均人手一機以上(資料樣本多)
◦ 不用額外的部署成本(成本低)
◦ 可適用全台灣地區(資料全面)
4
Data Network
RNS
GPRS/GSM MS
UE
BTS
Node B
BSC
RNC
Um
Uu Iub
A-bis
BSS
MSC/VLR
SGSN GGSN
HLR
Gn
GcGrGs
A
IuPS
IuCS
Gb
Core Network
PSTN
Cellular Network Signal
Retriever
Traffic Information
Estimation Methods
Vehicle Speed Forecasting
Method
ITS
GSM: Global System for Mobile Communications
GPRS: General Packet Radio SerVice
MS: Mobile Station
BTS: Base Transceiver Station
BSC: Base Station Controller
MSC: Mobile Switching Center
SGSN: Serving GPRS Support Node
GGSN: Gateway GPRS Support Node
VLR: Visitor Location Register
HSS: Home Subscriber Server
HLR: Home Location Register
PSTN: Public Switched Telephone Network
UE: User Equipment
RNC: Radio Network Controller
ITS: Intelligent Transportation System
8. 研究背景-蜂巢流動車輛資料
常見蜂巢流動車輛資料方法
◦ 運用兩次交遞(handoff, HO)估計車速[1, 2]
◦ 運用位置更新(location update, LU)估計流量[3]
8
Cell1
HO1 at location L1
at time t1
HO2 at location L2
at time t2
Road
Cell2 Cell3
Call arrival
at time t0
MS
Call completion
at time t3
Accident
12
21
2,1
),(
tt
LLd
U
1. D. Gundlegård and J. M. Karlsson, "Handover location accuracy for travel time estimation in GSM and UMTS", Sweden, 2009.
2. H. Bar-Gera, "Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: A case study from Israel", Israel, 2007.
3. D. Valerio, T. Witek, F. Ricciato, R. Pilz, W. Wiedermann, "Road traffic estimation from cellular network monitoring: a hands-on investigation", IEEE 20th Personal Indoor Mobile Radio
Communication Symposium 2009, Tokyo, Japan, 2009.
運用兩次交遞估計車速 運用位置更新估計流量
33. 研究背景-神經網路(梯度下降法)
33
w1 Y
梯度下降法應用於神經網路之權重和誤差項 採用線性函式
bxwy
i
ii
2
1
神經網路函式
(真值)
22
2
1
ˆ
2
1ˆ,ˆ yybwF
神經網路函式
(估計值)
目標函式
bz
bxwy
i
ii
ˆˆ
ˆˆˆ
2
1
函式切線斜率(對 偏微分)1
ˆw
修正方式1
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11
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函式切線斜率(對 偏微分)2
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2
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22
11
ˆ
ˆ
ˆ
ˆ
ˆˆ
x
x
w
z
z
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函式切線斜率(對 偏微分)bˆ
111
ˆ
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z
z
y
y
F
b
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修正方式2
ˆw
22
2
22
ˆ
ˆ
ˆˆ xw
w
F
ww
修正方式bˆ
b
b
F
bb ˆ
ˆ
ˆˆ
34. 研究背景-神經網路(梯度下降法)
34
w1 Y
梯度下降法應用於神經網路之權重和誤差項 採用S型函式
z
i
ii
e
zsy
bxwz
1
1
2
1
神經網路函式
(真值)
22
2
1
ˆ
2
1ˆ,ˆ yybwF
神經網路函式
(估計值)
目標函式
X2
X1
w2
z
i
ii
e
zsy
bxwz
ˆ
2
1
1
1
ˆˆ
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修正方式1
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xzszs
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zs
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y
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w
F
函式切線斜率(對 偏微分)2
ˆw 函式切線斜率(對 偏微分)bˆ
修正方式2
ˆw 修正方式bˆ
2
2
2
22
ˆ
ˆ1ˆ
ˆ
ˆ
11
ˆ
ˆ
ˆ
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xzs
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x
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b
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35. 神經網路與神經元
研究背景-神經網路(梯度下降法)
35
‧
‧
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‧
‧
‧
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‧
‧
‧
‧
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l
z1
l
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iz
l
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z
1
+
l
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a2
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ia
l
sl
a
l
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l
jiw ,
l
jw ,2
l
jSl
w ,
l
jb
1l
jz 1l
ja
第l層 第l+1層
第l+1層zj值(加權總和後)
l
i
s
i
l
i
l
ji
l
j bawz
l
1
,
1
第l+1層aj值(激活函式計算後)
l
i
s
i
l
i
l
ji
l
j bawga
l
1
,
1
激活函式(activation function)可為
線性、S型函式、或其他
xxg
x
e
xg
1
1
0if,0
0if,
x
xx
xg
線性函式
S型函式
線性整流函數
(Rectified
Linear Unit,
ReLU)
換個表示方式
36. 研究背景-神經網路(梯度下降法)
監督式學習目標為最小化估計值與真實值之間的誤差(損失)
◦ 假設損失函式(loss function)為
◦ 為真實值, 為估計值
◦ 最小化損失函式,計算方式為對損失函式微分
由於損失函式為多參數組成之函式,故分別對不同參數做偏微分
◦ 對 值計算偏微分,取得第l層最小誤差
◦ 對 值計算偏微分,取得第l層最小誤差之最佳 值
◦ 對 值計算偏微分,取得第l層最小誤差之最佳 值
對 值計算偏微分之數學證明
36
y yˆ
l
iz
l
jiw ,
l
jb
l
iz
1
1
1
1
1
,
1
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az
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l
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l
jb
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誤
差
或
損
失
全域最佳解
區域最佳解
0and
otherwise,0
f,1
where
l
i
l
j
l
i
l
k
a
bkii
a
a
37. 研究背景-神經網路(梯度下降法)
對 值計算偏微分之數學證明
對 值計算偏微分之數學證明
37
1
1,
,
1
,
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,
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j
l
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j
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baw
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0and
otherwise,0
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where
,,
,
l
ji
l
j
l
ji
l
jk
w
bkii
w
w
1
1,
1
1
l
j
l
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l
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j
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,
l
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,
,,
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l
j
l
j
l
j
b
F
bb
誤差項值修正
為學習率
避免一次跳太遠,
而錯過全域最佳解
為學習率
避免一次跳太遠,
而錯過全域最佳解
38. 研究方法
流量估計方法
◦ 運用一般位置更新(normal location update, NLU)
◦ 運用交遞(handoff, HO)
密度估計方法
◦ 運用通話到達(call arrival, CA)
◦ 運用週期位置更新(periodic location update, PLU)
車速估計方法
◦ 運用估計流量和估計密度
◦ 基於一般位置更新、通話到達
◦ 基於一般位置更新、週期位置更新
◦ 基於交遞、通話到達
◦ 基於交遞、週期位置更新
38
Data Network
RNS
GPRS/GSM MS
UE
BTS
Node B
BSC
RNC
Um
Uu Iub
A-bis
BSS
MSC/VLR
SGSN GGSN
HLR
Gn
GcGrGs
A
IuPS
IuCS
Gb
Core Network
PSTN
Cellular Network Signal
Retriever
Traffic Information
Estimation Methods
Vehicle Speed Forecasting
Method
ITS
K
Q
U
U
Q
K
40. 研究方法
Assumptions
◦ The call arrivals to/from one MS per one car along the road can be evaluated. The call holding time t is
exponentially distributed with the mean 1/m.
◦ The real traffic flow Qi, real vehicle speed Ui , and real traffic density Ki along the road can be obtained
from the VD data on the road segment covered by Celli.
◦ The mileage x is the time difference traveled from the first call arrival location to entering the specific
cell.
◦ The mileage li is the distance of road segment covered by Celli.
Outputs
◦ hi: The amount of HOs on the road segment covered by the specific cell.
◦ ai: The amount of CAs on the road segment covered by the specific cell.
◦ pi: The amount of PLUs on the road segment covered by the specific cell.
40
41. 研究方法
流量估計方法
◦ 運用一般位置更新(normal location update, NLU)
41
Cell1 Cell4
Normal Location
Update at time t1
1
21
Cell2
Normal Location
Update at location L1
Road
Cell3
a
BSC/RNC
LA1
2
LA2
b
ini Qq ,
42. 研究方法
流量估計方法
◦ 運用交遞(handoff, HO)
42
m
m m i
x xt
t
i
x
ii
Q
dxdteQ
dxxtQh
0
0
Pr
Call Arrival Call Departure
t
li/Ui
enter Celli leave Celli
x
t0 t1 t2 t3
m ihi hq ,
43. 研究方法
密度估計方法
◦ 運用通話到達(call arrival, CA)
43
1
1st
Call Arrival
at t0
Road
Celli
2nd
Call Arrival
at t2
Entering Cell
at t1
Leaving Cell
at t3
BSC/RNC
b
1
a
1st
Call Arrival 2nd
Call Arrival
t
li/Ui
Entering Celli Leaving Celli
x
t0 t1 t2 t3
44. 研究方法
密度估計方法
◦ 運用通話到達(call arrival, CA)
44
i
U
U
i
i
i
l
i
i
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i
U
U
i
l
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1
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1
11
111
1
1
Pr
Pr
1
1
1
1
1
1
0
0
321
t
t
t
t
i
U
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l
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e
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Q
q
a
qk
i
i
i
1
1limherew
,-1-1
1
1
,
1st
Call Arrival 2nd
Call Arrival
t
li/Ui
Entering Celli Leaving Celli
x
t0 t1 t2 t3
45. 研究方法
密度估計方法
◦ 運用週期位置更新(periodic location update, PLU)
◦ Scenario (1): No Call between Two Consecutive PLU Events
◦ Scenario (2): Several Calls between Two Consecutive PLU Events
◦ Consider Scenarios (1) and (2)
45
1st
Location Update
at t0
Road
Cell
2nd
Location Update
at t2
enter Cell at t1 leave Cell at t3
46. 研究方法
密度估計方法
◦ 運用週期位置更新(periodic location update, PLU)
◦ Scenario (1): No Call between Two Consecutive PLU Events
◦ Scenario (2): Several Calls between Two Consecutive PLU Events
◦ Consider Scenarios (1) and (2)
46
Ub
l
dx
b
dxxf
b
U
l
x
b
U
l
bx
b
U
l
bx
1
)(
Pr
)1ScenarioPr(
1st Location Update 2nd Location Update
b
l/U
enter Cell leave Cell
x
t0 t1 t2 t3
47. 研究方法
密度估計方法
◦ 運用週期位置更新(periodic location update, PLU)
◦ Scenario (1): No Call between Two Consecutive PLU Events
◦ Scenario (2): Several Calls between Two Consecutive PLU Events
◦ Consider Scenarios (1) and (2)
47
1st
call arrival
at t1
Road
Cell
2nd
Location Update
at t3
Enter Cell
at t2
Leave Cell
at t4
1st
Location Update
at t0
2nd
call arrival
at t5
48. 研究方法
密度估計方法
◦ 運用週期位置更新(periodic location update, PLU)
◦ Scenario (1): No Call between Two Consecutive PLU Events
◦ Scenario (2): Several Calls between Two Consecutive PLU Events
◦ Consider Scenarios (1) and (2)
48
Ub
l
e
Ub
l
de
dyyhdg
U
l
byb
b
U
l
yb
b
b
b
U
l
byb
2
2
)(
PrPr
Pr
)2ScenarioPr(
t
t
t
t
tt
t
t
1st call arrival 2nd Location Update
b
l/U
enter Cell leave Cell
y
t1 t2 t3 t4
1st Location Update
t0
2nd call arrival
t5
t
49. 研究方法
密度估計方法
◦ 運用週期位置更新(periodic location update, PLU)
◦ Scenario (1): No Call between Two Consecutive PLU Events
◦ Scenario (2): Several Calls between Two Consecutive PLU Events
◦ Consider Scenarios (1) and (2)
49
Ub
l
ee
Ub
l
ee
Ub
l
e
bb
bb
bbb
2
3
2
1
)2SenarioPr(Pr)1SenarioPr(Pr)PLUPr(
tt
bU
l
eeK
bU
l
eeQ
Qp
i
bb
i
i
bb
i
ii
2
3
2
3
)PLUPr(
b
l
ee
p
k
bb
i
pi
2
3
,
50. 研究方法
車速估計方法
◦ 運用估計流量和估計密度
◦ 基於一般位置更新、通話到達
◦ 基於一般位置更新、週期位置更新
◦ 基於交遞、通話到達
◦ 基於交遞、週期位置更新
50
11
,,
,
-1-1
il
ni
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ai
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q
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k
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u
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,
,
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h
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,
,
i
bb
i
pi
hi
i
p
b
l
eeh
k
q
u
2
3
,
,
m
71. 實驗結果與討論
實驗環境
◦ 運用VISSIM軟體產生車輛移動紀錄
◦ 運用亂數產生器產生通聯紀錄
◦ 分析車輛移動和通聯紀錄
71
Cell1 Cell2 Cell3 Cell4 Cell5 Cell6 Cell7 Cell8 Cell9 Cell10
LA1 LA2 LA3 LA4
r1 r2 r3 r4 r5 r6 r7 r8 r9 r10
Road
The amount of NLU
in LA1 q1=q2=q3
The amount of NLU
in LA2 q4=q5=q6
The amount of NLU
in LA3 q7=q8=q9
The amount of
NLU in LA4 q10
VD1 VD2 VD3 VD4 VD5 VD6 VD7 VD8 VD9 VD10
Road Conditions and
Vehicle Movement
Behaviors
Trace File
Vehicle Movement
Trace Generation
MS Communication
Behaviors
MS Communication
Trace Generation
Vehicle movement
and
MS communication
Trace Generation
Trace File Traffic Information
Estimation