Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem

Nowadays, traffic engineers employ a variety of intelligent tools for decision support in the field of transportation planning and management. However, not a one available tool is useful without precise travel demand information which is actually the key input data in simulation models used for traf...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Krylatov A. [verfasserIn] Raevskaya A. [verfasserIn] Zakharov V. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	travel demand estimation od-matrix estimation congestion traffic assignment problem primal and inverse problems

Übergeordnetes Werk:	In: Transport and Telecommunication - Sciendo, 2012, 22(2021), 3, Seite 287-300
Übergeordnetes Werk:	volume:22 ; year:2021 ; number:3 ; pages:287-300

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.2478/ttj-2021-0022

Katalog-ID:	DOAJ048338516

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ048338516
003	DE-627
005	20230503071355.0
007	cr uuu---uuuuu
008	230227s2021 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.2478/ttj-2021-0022 \|2 doi
035			\|a (DE-627)DOAJ048338516
035			\|a (DE-599)DOAJ0b96cc8b64094d029a564a59e1355489
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a K4011-4343
100	0		\|a Krylatov A. \|e verfasserin \|4 aut
245	1	0	\|a Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem
264		1	\|c 2021
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Nowadays, traffic engineers employ a variety of intelligent tools for decision support in the field of transportation planning and management. However, not a one available tool is useful without precise travel demand information which is actually the key input data in simulation models used for traffic prediction in urban road areas. Thus, it is no wonder that the problem of estimation of travel demand values between intersections in a road network is a challenge of high urgency. The present paper is devoted to this urgent problem and investigates its properties from computational and mathematical perspectives. We rigorously define the travel demand estimation problem as directly inverse to traffic assignment in a form of a bi-level optimization program avoiding usage of any pre-given (a priori) information on trips. The computational study of the obtained optimization program demonstrates that generally it has no clear descent direction, while the mathematical study advances our understanding on rigor existence and uniqueness conditions of its solution. We prove that once a traffic engineer recognizes the travel demand locations, then their values in the road network can be found uniquely. On the contrary, we discover a non-continuous dependence between the travel demand locations and absolute difference of observed and modeled traffic values. Therefore, the results of the present paper reveal that the actual problem to be solved when dealing with travel demand estimation is the problem of recognition of travel demand locations. The obtained findings contribute in the theory of travel demand estimation and give fresh managerial insights for traffic engineers.
650		4	\|a travel demand estimation
650		4	\|a od-matrix estimation
650		4	\|a congestion
650		4	\|a traffic assignment problem
650		4	\|a primal and inverse problems
653		0	\|a Transportation and communication
700	0		\|a Raevskaya A. \|e verfasserin \|4 aut
700	0		\|a Zakharov V. \|e verfasserin \|4 aut
773	0	8	\|i In \|t Transport and Telecommunication \|d Sciendo, 2012 \|g 22(2021), 3, Seite 287-300 \|w (DE-627)722237057 \|w (DE-600)2677597-9 \|x 14076179 \|7 nnns
773	1	8	\|g volume:22 \|g year:2021 \|g number:3 \|g pages:287-300
856	4	0	\|u https://doi.org/10.2478/ttj-2021-0022 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/0b96cc8b64094d029a564a59e1355489 \|z kostenfrei
856	4	0	\|u https://doi.org/10.2478/ttj-2021-0022 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/1407-6179 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 22 \|j 2021 \|e 3 \|h 287-300

Indexfelder

author_variant	k a ka r a ra z v zv
matchkey_str	article:14076179:2021----::rvleadsiainnrarantokaivrerf
hierarchy_sort_str	2021
callnumber-subject-code	K
publishDate	2021
allfields	10.2478/ttj-2021-0022 doi (DE-627)DOAJ048338516 (DE-599)DOAJ0b96cc8b64094d029a564a59e1355489 DE-627 ger DE-627 rakwb eng K4011-4343 Krylatov A. verfasserin aut Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Nowadays, traffic engineers employ a variety of intelligent tools for decision support in the field of transportation planning and management. However, not a one available tool is useful without precise travel demand information which is actually the key input data in simulation models used for traffic prediction in urban road areas. Thus, it is no wonder that the problem of estimation of travel demand values between intersections in a road network is a challenge of high urgency. The present paper is devoted to this urgent problem and investigates its properties from computational and mathematical perspectives. We rigorously define the travel demand estimation problem as directly inverse to traffic assignment in a form of a bi-level optimization program avoiding usage of any pre-given (a priori) information on trips. The computational study of the obtained optimization program demonstrates that generally it has no clear descent direction, while the mathematical study advances our understanding on rigor existence and uniqueness conditions of its solution. We prove that once a traffic engineer recognizes the travel demand locations, then their values in the road network can be found uniquely. On the contrary, we discover a non-continuous dependence between the travel demand locations and absolute difference of observed and modeled traffic values. Therefore, the results of the present paper reveal that the actual problem to be solved when dealing with travel demand estimation is the problem of recognition of travel demand locations. The obtained findings contribute in the theory of travel demand estimation and give fresh managerial insights for traffic engineers. travel demand estimation od-matrix estimation congestion traffic assignment problem primal and inverse problems Transportation and communication Raevskaya A. verfasserin aut Zakharov V. verfasserin aut In Transport and Telecommunication Sciendo, 2012 22(2021), 3, Seite 287-300 (DE-627)722237057 (DE-600)2677597-9 14076179 nnns volume:22 year:2021 number:3 pages:287-300 https://doi.org/10.2478/ttj-2021-0022 kostenfrei https://doaj.org/article/0b96cc8b64094d029a564a59e1355489 kostenfrei https://doi.org/10.2478/ttj-2021-0022 kostenfrei https://doaj.org/toc/1407-6179 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2021 3 287-300
spelling	10.2478/ttj-2021-0022 doi (DE-627)DOAJ048338516 (DE-599)DOAJ0b96cc8b64094d029a564a59e1355489 DE-627 ger DE-627 rakwb eng K4011-4343 Krylatov A. verfasserin aut Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Nowadays, traffic engineers employ a variety of intelligent tools for decision support in the field of transportation planning and management. However, not a one available tool is useful without precise travel demand information which is actually the key input data in simulation models used for traffic prediction in urban road areas. Thus, it is no wonder that the problem of estimation of travel demand values between intersections in a road network is a challenge of high urgency. The present paper is devoted to this urgent problem and investigates its properties from computational and mathematical perspectives. We rigorously define the travel demand estimation problem as directly inverse to traffic assignment in a form of a bi-level optimization program avoiding usage of any pre-given (a priori) information on trips. The computational study of the obtained optimization program demonstrates that generally it has no clear descent direction, while the mathematical study advances our understanding on rigor existence and uniqueness conditions of its solution. We prove that once a traffic engineer recognizes the travel demand locations, then their values in the road network can be found uniquely. On the contrary, we discover a non-continuous dependence between the travel demand locations and absolute difference of observed and modeled traffic values. Therefore, the results of the present paper reveal that the actual problem to be solved when dealing with travel demand estimation is the problem of recognition of travel demand locations. The obtained findings contribute in the theory of travel demand estimation and give fresh managerial insights for traffic engineers. travel demand estimation od-matrix estimation congestion traffic assignment problem primal and inverse problems Transportation and communication Raevskaya A. verfasserin aut Zakharov V. verfasserin aut In Transport and Telecommunication Sciendo, 2012 22(2021), 3, Seite 287-300 (DE-627)722237057 (DE-600)2677597-9 14076179 nnns volume:22 year:2021 number:3 pages:287-300 https://doi.org/10.2478/ttj-2021-0022 kostenfrei https://doaj.org/article/0b96cc8b64094d029a564a59e1355489 kostenfrei https://doi.org/10.2478/ttj-2021-0022 kostenfrei https://doaj.org/toc/1407-6179 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2021 3 287-300
allfields_unstemmed	10.2478/ttj-2021-0022 doi (DE-627)DOAJ048338516 (DE-599)DOAJ0b96cc8b64094d029a564a59e1355489 DE-627 ger DE-627 rakwb eng K4011-4343 Krylatov A. verfasserin aut Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Nowadays, traffic engineers employ a variety of intelligent tools for decision support in the field of transportation planning and management. However, not a one available tool is useful without precise travel demand information which is actually the key input data in simulation models used for traffic prediction in urban road areas. Thus, it is no wonder that the problem of estimation of travel demand values between intersections in a road network is a challenge of high urgency. The present paper is devoted to this urgent problem and investigates its properties from computational and mathematical perspectives. We rigorously define the travel demand estimation problem as directly inverse to traffic assignment in a form of a bi-level optimization program avoiding usage of any pre-given (a priori) information on trips. The computational study of the obtained optimization program demonstrates that generally it has no clear descent direction, while the mathematical study advances our understanding on rigor existence and uniqueness conditions of its solution. We prove that once a traffic engineer recognizes the travel demand locations, then their values in the road network can be found uniquely. On the contrary, we discover a non-continuous dependence between the travel demand locations and absolute difference of observed and modeled traffic values. Therefore, the results of the present paper reveal that the actual problem to be solved when dealing with travel demand estimation is the problem of recognition of travel demand locations. The obtained findings contribute in the theory of travel demand estimation and give fresh managerial insights for traffic engineers. travel demand estimation od-matrix estimation congestion traffic assignment problem primal and inverse problems Transportation and communication Raevskaya A. verfasserin aut Zakharov V. verfasserin aut In Transport and Telecommunication Sciendo, 2012 22(2021), 3, Seite 287-300 (DE-627)722237057 (DE-600)2677597-9 14076179 nnns volume:22 year:2021 number:3 pages:287-300 https://doi.org/10.2478/ttj-2021-0022 kostenfrei https://doaj.org/article/0b96cc8b64094d029a564a59e1355489 kostenfrei https://doi.org/10.2478/ttj-2021-0022 kostenfrei https://doaj.org/toc/1407-6179 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2021 3 287-300
allfieldsGer	10.2478/ttj-2021-0022 doi (DE-627)DOAJ048338516 (DE-599)DOAJ0b96cc8b64094d029a564a59e1355489 DE-627 ger DE-627 rakwb eng K4011-4343 Krylatov A. verfasserin aut Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Nowadays, traffic engineers employ a variety of intelligent tools for decision support in the field of transportation planning and management. However, not a one available tool is useful without precise travel demand information which is actually the key input data in simulation models used for traffic prediction in urban road areas. Thus, it is no wonder that the problem of estimation of travel demand values between intersections in a road network is a challenge of high urgency. The present paper is devoted to this urgent problem and investigates its properties from computational and mathematical perspectives. We rigorously define the travel demand estimation problem as directly inverse to traffic assignment in a form of a bi-level optimization program avoiding usage of any pre-given (a priori) information on trips. The computational study of the obtained optimization program demonstrates that generally it has no clear descent direction, while the mathematical study advances our understanding on rigor existence and uniqueness conditions of its solution. We prove that once a traffic engineer recognizes the travel demand locations, then their values in the road network can be found uniquely. On the contrary, we discover a non-continuous dependence between the travel demand locations and absolute difference of observed and modeled traffic values. Therefore, the results of the present paper reveal that the actual problem to be solved when dealing with travel demand estimation is the problem of recognition of travel demand locations. The obtained findings contribute in the theory of travel demand estimation and give fresh managerial insights for traffic engineers. travel demand estimation od-matrix estimation congestion traffic assignment problem primal and inverse problems Transportation and communication Raevskaya A. verfasserin aut Zakharov V. verfasserin aut In Transport and Telecommunication Sciendo, 2012 22(2021), 3, Seite 287-300 (DE-627)722237057 (DE-600)2677597-9 14076179 nnns volume:22 year:2021 number:3 pages:287-300 https://doi.org/10.2478/ttj-2021-0022 kostenfrei https://doaj.org/article/0b96cc8b64094d029a564a59e1355489 kostenfrei https://doi.org/10.2478/ttj-2021-0022 kostenfrei https://doaj.org/toc/1407-6179 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2021 3 287-300
allfieldsSound	10.2478/ttj-2021-0022 doi (DE-627)DOAJ048338516 (DE-599)DOAJ0b96cc8b64094d029a564a59e1355489 DE-627 ger DE-627 rakwb eng K4011-4343 Krylatov A. verfasserin aut Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Nowadays, traffic engineers employ a variety of intelligent tools for decision support in the field of transportation planning and management. However, not a one available tool is useful without precise travel demand information which is actually the key input data in simulation models used for traffic prediction in urban road areas. Thus, it is no wonder that the problem of estimation of travel demand values between intersections in a road network is a challenge of high urgency. The present paper is devoted to this urgent problem and investigates its properties from computational and mathematical perspectives. We rigorously define the travel demand estimation problem as directly inverse to traffic assignment in a form of a bi-level optimization program avoiding usage of any pre-given (a priori) information on trips. The computational study of the obtained optimization program demonstrates that generally it has no clear descent direction, while the mathematical study advances our understanding on rigor existence and uniqueness conditions of its solution. We prove that once a traffic engineer recognizes the travel demand locations, then their values in the road network can be found uniquely. On the contrary, we discover a non-continuous dependence between the travel demand locations and absolute difference of observed and modeled traffic values. Therefore, the results of the present paper reveal that the actual problem to be solved when dealing with travel demand estimation is the problem of recognition of travel demand locations. The obtained findings contribute in the theory of travel demand estimation and give fresh managerial insights for traffic engineers. travel demand estimation od-matrix estimation congestion traffic assignment problem primal and inverse problems Transportation and communication Raevskaya A. verfasserin aut Zakharov V. verfasserin aut In Transport and Telecommunication Sciendo, 2012 22(2021), 3, Seite 287-300 (DE-627)722237057 (DE-600)2677597-9 14076179 nnns volume:22 year:2021 number:3 pages:287-300 https://doi.org/10.2478/ttj-2021-0022 kostenfrei https://doaj.org/article/0b96cc8b64094d029a564a59e1355489 kostenfrei https://doi.org/10.2478/ttj-2021-0022 kostenfrei https://doaj.org/toc/1407-6179 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2021 3 287-300
language	English
source	In Transport and Telecommunication 22(2021), 3, Seite 287-300 volume:22 year:2021 number:3 pages:287-300
sourceStr	In Transport and Telecommunication 22(2021), 3, Seite 287-300 volume:22 year:2021 number:3 pages:287-300
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	travel demand estimation od-matrix estimation congestion traffic assignment problem primal and inverse problems Transportation and communication
isfreeaccess_bool	true
container_title	Transport and Telecommunication
authorswithroles_txt_mv	Krylatov A. @@aut@@ Raevskaya A. @@aut@@ Zakharov V. @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	722237057
id	DOAJ048338516
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ048338516</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503071355.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.2478/ttj-2021-0022</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ048338516</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ0b96cc8b64094d029a564a59e1355489</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">K4011-4343</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Krylatov A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Nowadays, traffic engineers employ a variety of intelligent tools for decision support in the field of transportation planning and management. However, not a one available tool is useful without precise travel demand information which is actually the key input data in simulation models used for traffic prediction in urban road areas. Thus, it is no wonder that the problem of estimation of travel demand values between intersections in a road network is a challenge of high urgency. The present paper is devoted to this urgent problem and investigates its properties from computational and mathematical perspectives. We rigorously define the travel demand estimation problem as directly inverse to traffic assignment in a form of a bi-level optimization program avoiding usage of any pre-given (a priori) information on trips. The computational study of the obtained optimization program demonstrates that generally it has no clear descent direction, while the mathematical study advances our understanding on rigor existence and uniqueness conditions of its solution. We prove that once a traffic engineer recognizes the travel demand locations, then their values in the road network can be found uniquely. On the contrary, we discover a non-continuous dependence between the travel demand locations and absolute difference of observed and modeled traffic values. Therefore, the results of the present paper reveal that the actual problem to be solved when dealing with travel demand estimation is the problem of recognition of travel demand locations. The obtained findings contribute in the theory of travel demand estimation and give fresh managerial insights for traffic engineers.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">travel demand estimation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">od-matrix estimation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">congestion</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">traffic assignment problem</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">primal and inverse problems</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Transportation and communication</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Raevskaya A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zakharov V.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Transport and Telecommunication</subfield><subfield code="d">Sciendo, 2012</subfield><subfield code="g">22(2021), 3, Seite 287-300</subfield><subfield code="w">(DE-627)722237057</subfield><subfield code="w">(DE-600)2677597-9</subfield><subfield code="x">14076179</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:22</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:3</subfield><subfield code="g">pages:287-300</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.2478/ttj-2021-0022</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/0b96cc8b64094d029a564a59e1355489</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.2478/ttj-2021-0022</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1407-6179</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">22</subfield><subfield code="j">2021</subfield><subfield code="e">3</subfield><subfield code="h">287-300</subfield></datafield></record></collection>
callnumber-first	K - Law
author	Krylatov A.
spellingShingle	Krylatov A. misc K4011-4343 misc travel demand estimation misc od-matrix estimation misc congestion misc traffic assignment problem misc primal and inverse problems misc Transportation and communication Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem
authorStr	Krylatov A.
ppnlink_with_tag_str_mv	@@773@@(DE-627)722237057
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	K4011-4343
illustrated	Not Illustrated
issn	14076179
topic_title	K4011-4343 Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem travel demand estimation od-matrix estimation congestion traffic assignment problem primal and inverse problems
topic	misc K4011-4343 misc travel demand estimation misc od-matrix estimation misc congestion misc traffic assignment problem misc primal and inverse problems misc Transportation and communication
topic_unstemmed	misc K4011-4343 misc travel demand estimation misc od-matrix estimation misc congestion misc traffic assignment problem misc primal and inverse problems misc Transportation and communication
topic_browse	misc K4011-4343 misc travel demand estimation misc od-matrix estimation misc congestion misc traffic assignment problem misc primal and inverse problems misc Transportation and communication
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Transport and Telecommunication
hierarchy_parent_id	722237057
hierarchy_top_title	Transport and Telecommunication
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)722237057 (DE-600)2677597-9
title	Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem
ctrlnum	(DE-627)DOAJ048338516 (DE-599)DOAJ0b96cc8b64094d029a564a59e1355489
title_full	Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem
author_sort	Krylatov A.
journal	Transport and Telecommunication
journalStr	Transport and Telecommunication
callnumber-first-code	K
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2021
contenttype_str_mv	txt
container_start_page	287
author_browse	Krylatov A. Raevskaya A. Zakharov V.
container_volume	22
class	K4011-4343
format_se	Elektronische Aufsätze
author-letter	Krylatov A.
doi_str_mv	10.2478/ttj-2021-0022
author2-role	verfasserin
title_sort	travel demand estimation in urban road networks as inverse traffic assignment problem
callnumber	K4011-4343
title_auth	Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem
abstract	Nowadays, traffic engineers employ a variety of intelligent tools for decision support in the field of transportation planning and management. However, not a one available tool is useful without precise travel demand information which is actually the key input data in simulation models used for traffic prediction in urban road areas. Thus, it is no wonder that the problem of estimation of travel demand values between intersections in a road network is a challenge of high urgency. The present paper is devoted to this urgent problem and investigates its properties from computational and mathematical perspectives. We rigorously define the travel demand estimation problem as directly inverse to traffic assignment in a form of a bi-level optimization program avoiding usage of any pre-given (a priori) information on trips. The computational study of the obtained optimization program demonstrates that generally it has no clear descent direction, while the mathematical study advances our understanding on rigor existence and uniqueness conditions of its solution. We prove that once a traffic engineer recognizes the travel demand locations, then their values in the road network can be found uniquely. On the contrary, we discover a non-continuous dependence between the travel demand locations and absolute difference of observed and modeled traffic values. Therefore, the results of the present paper reveal that the actual problem to be solved when dealing with travel demand estimation is the problem of recognition of travel demand locations. The obtained findings contribute in the theory of travel demand estimation and give fresh managerial insights for traffic engineers.
abstractGer	Nowadays, traffic engineers employ a variety of intelligent tools for decision support in the field of transportation planning and management. However, not a one available tool is useful without precise travel demand information which is actually the key input data in simulation models used for traffic prediction in urban road areas. Thus, it is no wonder that the problem of estimation of travel demand values between intersections in a road network is a challenge of high urgency. The present paper is devoted to this urgent problem and investigates its properties from computational and mathematical perspectives. We rigorously define the travel demand estimation problem as directly inverse to traffic assignment in a form of a bi-level optimization program avoiding usage of any pre-given (a priori) information on trips. The computational study of the obtained optimization program demonstrates that generally it has no clear descent direction, while the mathematical study advances our understanding on rigor existence and uniqueness conditions of its solution. We prove that once a traffic engineer recognizes the travel demand locations, then their values in the road network can be found uniquely. On the contrary, we discover a non-continuous dependence between the travel demand locations and absolute difference of observed and modeled traffic values. Therefore, the results of the present paper reveal that the actual problem to be solved when dealing with travel demand estimation is the problem of recognition of travel demand locations. The obtained findings contribute in the theory of travel demand estimation and give fresh managerial insights for traffic engineers.
abstract_unstemmed	Nowadays, traffic engineers employ a variety of intelligent tools for decision support in the field of transportation planning and management. However, not a one available tool is useful without precise travel demand information which is actually the key input data in simulation models used for traffic prediction in urban road areas. Thus, it is no wonder that the problem of estimation of travel demand values between intersections in a road network is a challenge of high urgency. The present paper is devoted to this urgent problem and investigates its properties from computational and mathematical perspectives. We rigorously define the travel demand estimation problem as directly inverse to traffic assignment in a form of a bi-level optimization program avoiding usage of any pre-given (a priori) information on trips. The computational study of the obtained optimization program demonstrates that generally it has no clear descent direction, while the mathematical study advances our understanding on rigor existence and uniqueness conditions of its solution. We prove that once a traffic engineer recognizes the travel demand locations, then their values in the road network can be found uniquely. On the contrary, we discover a non-continuous dependence between the travel demand locations and absolute difference of observed and modeled traffic values. Therefore, the results of the present paper reveal that the actual problem to be solved when dealing with travel demand estimation is the problem of recognition of travel demand locations. The obtained findings contribute in the theory of travel demand estimation and give fresh managerial insights for traffic engineers.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	3
title_short	Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem
url	https://doi.org/10.2478/ttj-2021-0022 https://doaj.org/article/0b96cc8b64094d029a564a59e1355489 https://doaj.org/toc/1407-6179
remote_bool	true
author2	Raevskaya A. Zakharov V.
author2Str	Raevskaya A. Zakharov V.
ppnlink	722237057
callnumber-subject	K - General Law
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.2478/ttj-2021-0022
callnumber-a	K4011-4343
up_date	2024-07-03T17:14:33.183Z
_version_	1803578898576834560
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ048338516</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503071355.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.2478/ttj-2021-0022</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ048338516</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ0b96cc8b64094d029a564a59e1355489</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">K4011-4343</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Krylatov A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Nowadays, traffic engineers employ a variety of intelligent tools for decision support in the field of transportation planning and management. However, not a one available tool is useful without precise travel demand information which is actually the key input data in simulation models used for traffic prediction in urban road areas. Thus, it is no wonder that the problem of estimation of travel demand values between intersections in a road network is a challenge of high urgency. The present paper is devoted to this urgent problem and investigates its properties from computational and mathematical perspectives. We rigorously define the travel demand estimation problem as directly inverse to traffic assignment in a form of a bi-level optimization program avoiding usage of any pre-given (a priori) information on trips. The computational study of the obtained optimization program demonstrates that generally it has no clear descent direction, while the mathematical study advances our understanding on rigor existence and uniqueness conditions of its solution. We prove that once a traffic engineer recognizes the travel demand locations, then their values in the road network can be found uniquely. On the contrary, we discover a non-continuous dependence between the travel demand locations and absolute difference of observed and modeled traffic values. Therefore, the results of the present paper reveal that the actual problem to be solved when dealing with travel demand estimation is the problem of recognition of travel demand locations. The obtained findings contribute in the theory of travel demand estimation and give fresh managerial insights for traffic engineers.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">travel demand estimation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">od-matrix estimation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">congestion</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">traffic assignment problem</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">primal and inverse problems</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Transportation and communication</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Raevskaya A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zakharov V.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Transport and Telecommunication</subfield><subfield code="d">Sciendo, 2012</subfield><subfield code="g">22(2021), 3, Seite 287-300</subfield><subfield code="w">(DE-627)722237057</subfield><subfield code="w">(DE-600)2677597-9</subfield><subfield code="x">14076179</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:22</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:3</subfield><subfield code="g">pages:287-300</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.2478/ttj-2021-0022</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/0b96cc8b64094d029a564a59e1355489</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.2478/ttj-2021-0022</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1407-6179</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">22</subfield><subfield code="j">2021</subfield><subfield code="e">3</subfield><subfield code="h">287-300</subfield></datafield></record></collection>
score	7.40158

Nicht das Richtige dabei?

Schreiben Sie uns!

Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?